The DOI matches its Crossref record; 69 of the 78 references it cites were found in a registry or an index; 13 of the 34 sources its claims cite were checked for support.
WoodScape: A Multi-Task, Multi-Camera Fisheye Dataset for Autonomous Driving
Open accessAbstract
Fisheye cameras are commonly employed for obtaining a large field of view in surveillance, augmented reality and in particular automotive applications. In spite of their prevalence, there are few public datasets for detailed evaluation of computer vision algorithms on fisheye images. We release the first extensive fisheye automotive dataset, WoodScape, named after Robert Wood who invented the fisheye camera in 1906. WoodScape comprises of four surround view cameras and nine tasks including segmentation, depth estimation, 3D bounding box detection and soiling detection. Semantic annotation of 40 classes at the instance level is provided for over 10,000 images and annotation for other tasks are provided for over 100,000 images. With WoodScape, we would like to encourage the community to adapt computer vision models for fisheye camera instead of using naive rectification.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
WoodScape is introduced as a multi-task, multi-camera fisheye dataset for autonomous driving, with four 190° horizontal-FOV cameras, LiDAR, GNSS/IMU, and annotations for nine tasks. It provides baseline results across segmentation, detection, depth, motion, soiling, odometry/SLAM, and 3D boxes, while arguing for native fisheye models rather than undistortion.
Key findings
- The dataset contains over 10,000 images with instance-level semantic annotation, four 1MPx RGB fisheye cameras covering 360°, one Velodyne HDL-64E LiDAR at 20 Hz, GNSS/IMU, and labels for nine tasks including semantic segmentation, 2D/3D detection, depth, motion segmentation, soiling detection, visual odometry/SLAM, and end-to-end driving.
- Baseline results include ENet segmentation IoU 51.4, Faster R-CNN 2D detection mAP 31 at IoU ≥ 0.5, ResNet10 soiling category precision 84.5%, Eigen depth RMSE 7.7, MODNet motion segmentation IoU 45, visual odometry translation <5 mm 51% and rotation <0.1° 71%, and LSD-SLAM relocalization 61%.
- For 3D bounding box detection, Complex-YOLO trained with the proposed S_srt metric achieved AP 62.46%, AOS 88.43%, and runtime 1 ms, versus 3D-IoU training with AP 64.38%, AOS 85.60%, and runtime 95 ms, improving training time by 95×.
- A pre-trained person detector achieved mAP 12 on fisheye images versus 45 for a model trained on WoodScape, which the authors use to argue that fisheye geometry must be handled explicitly.
Methods
- The dataset was captured from three geographical locations (USA, Europe, China), with highway, urban, and parking scenarios, saloon and sports utility vehicles, and intrinsic/extrinsic calibration plus timestamp synchronization.
- Annotation covers 40 semantic classes, 5k soiling images, 10k 3D box frames, 400k depth frames, 10k motion segmentation frames, 50 video sequences with over 100k frames for VO/SLAM, 500 end-to-end driving videos, and 10k synthetic frames.
- Data splitting is 6:1:3 training/validation/testing, with five splits and task-specific stratified splits; sampling uses a wrapper method based on classifier accuracy plus a filter based on image similarity. Baselines use ENet, Faster R-CNN with ResNet101, ResNet10, Eigen, MODNet, LSD-SLAM, and Complex-YOLO.
Limitations
- The paper states that 2D bounding box detection mAP of 31 is significantly lower than on other datasets and that incorporating fisheye geometry explicitly remains an open research problem.
- The paper states that spherical models do not accurately fit fisheye lenses and that adapting CNNs to fisheye distortion is an open problem; it also notes that simultaneous annotation is provided for various tasks but with exceptions due to practical limitations of optimal dataset design.
- The paper states that more than one third of the dataset is recorded in Europe and is GDPR-sensitive due to visible faces and license plates, so it follows a license-agreement approach rather than blurring or GAN retargeting; it also states that end-to-end steering/braking is not currently mature for deployment.
Model-written summary. Check it against the paper before relying on it.
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Claims the paper makes
13 of 34 cited sources checked · 0 supported · 1 flagged · accuracy not yet measured
At least one cited source doesn’t support this claim or points to a different paper, and none confirmed it — see the verdicts below.
In the paper
“We run the semi-supervised approach from [33] using the model proposed by Eigen [14] as baseline on our much larger dataset and obtained an RMSE (Root Mean Square Error) value of 7.7.”
§ WoodScape: A multi-task, multi-camera fisheye dataset for autonomous driving > 4.5. Monocular Depth Estimation
What the sources say
- Not checked — no full text of the sourcecites [33] source
- Disputedcites [14] sourceExperimental — accuracy not yet measured
The citation in the sentence attributes the semi-supervised approach to this source; the RMSE is presented as the citing authors’ own result. Section 2 (pp. 2–3) instead describes training a network on images paired with human steering commands to predict steering, not a semi-supervised approach to monocular depth estimation. (LLM, conf=0.86)
Checked by a reasoning model — may be wrong.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesThe cited sources that could be checked couldn’t confirm or dispute this claim.
In the paper
“| Soiling Detection | ResNet10 [20] | Category (%) | 84.5 |”
§ WoodScape: A multi-task, multi-camera fisheye dataset for autonomous driving > 5. Conclusions
What the sources say
- Could not verifycites [20] sourceExperimental — accuracy not yet measured
The paper’s discussion of classification methods (III. Vision Algorithm Building Blocks, p. 4) and its table of established applications (p. 5) do not report a soiling-detection result. Searches for WoodScape, ResNet10, and 84.5 found no passage supporting the attributed dataset, model, or score. (LLM, conf=0.76)
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Could not verify
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Depending on the particular task (see Section 4, for the full list), the class imbalance may be an issue [19], therefore, task-specific splits are also provided.”
§ WoodScape: A multi-task, multi-camera fisheye dataset for autonomous driving > Data splitting and class balancing:
What the sources say
- Partly supportedcites [19] sourceExperimental — accuracy not yet measured
Section 5.1 (p. 12) identifies a class-imbalance problem: traffic lights occur less often than light poles, and the authors suggest better class-balancing strategies. Section 5.2 (p. 12) describes separate task benchmarks and dataset splits, but does not say those splits were provided because of class imbalance. (LLM, conf=0.84)
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Partly supported
The cited sources that could be checked couldn’t confirm or dispute this claim.
In the paper
“| Visual Odometry | ResNet50 [20] | Translation (<5mm) Rotation (<0.1°) | 51 71 |”
§ WoodScape: A multi-task, multi-camera fisheye dataset for autonomous driving > 5. Conclusions
What the sources say
- Could not verifycites [20] sourceExperimental — accuracy not yet measured
The discussion of odometry in 'Towards a generic scalable algorithm framework' (p. 6) concerns its use in segmenting moving objects, not a performance evaluation. Searches for WoodScape, ResNet50, and the claimed translation threshold and score found no passage in our copy reporting that result. (LLM, conf=0.77)
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Could not verify
The cited sources that could be checked couldn’t confirm or dispute this claim.
In the paper
“| Visual Odometry | ResNet50 [20] | Translation (<5mm) Rotation (<0.1°) | 51 71 |”
§ WoodScape: A multi-task, multi-camera fisheye dataset for autonomous driving > 5. Conclusions
What the sources say
- Could not verifycites [20] sourceExperimental — accuracy not yet measured
The Motion Estimation section (p. 3) describes tracking and matching image pixels, but reports no visual-odometry benchmark score. Searches for WoodScape, ResNet50, and the stated rotation and translation thresholds found no passage supporting the score of 71. (LLM, conf=0.76)
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Could not verify
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“First fisheye dataset comprising of over 10,000 images containing instance level semantic annotation.”
§ WoodScape: A multi-task, multi-camera fisheye dataset for autonomous driving > 1. Introduction
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesThe paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Proposal of an efficient metric for the 3D box detection task which improves training time by 95x.”
§ WoodScape: A multi-task, multi-camera fisheye dataset for autonomous driving > 1. Introduction
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes? Based: unclearA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Annotations are made available for nine tasks, notably 3D object detection, depth estimation (overlaid on front camera) and semantic segmentation as illustrated here.”
- Also stated in: § 3.2. Dataset Acquisition · plain
“Our WoodScape dataset provides labels for several autonomous driving tasks including semantic segmentation, monocular depth estimation, object detection (2D & 3D bounding boxes), visual odometry, visual SLAM, motion segmentation, soiling detection and end-to-end driving (driving controls).”
§ WoodScape: A multi-task, multi-camera fisheye dataset for autonomous driving > Abstract
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Also stated in: § 3.2. Dataset Acquisition · plain
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Our model is based on a fourth order polynomial mapping incident angle to image radius in pixels (r(θ) = a 1 θ + a 2 θ 2 + a 3 θ 3 + a 4 θ 4 ).”
§ WoodScape: A multi-task, multi-camera fisheye dataset for autonomous driving > 2.1. Fisheye Camera Models
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesThe paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“We have designed our dataset so that simultaneous annotation is provided for various tasks with some exceptions due to practical limitations of optimal dataset design for each task.”
§ WoodScape: A multi-task, multi-camera fisheye dataset for autonomous driving > 3.1. High-Level Goals
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes
- It may state two separate claims as one. 36
- Probably not a substantial claim. 19
The cited sources that could be checked couldn’t confirm or dispute this claim.
In the paper
“There are very few public fisheye datasets and none of them provide semantic segmentation annotation.”
§ WoodScape: A multi-task, multi-camera fisheye dataset for autonomous driving > 3.1. High-Level Goals
What the sources say
- Could not verifycites [7] sourceExperimental — accuracy not yet measured
The RobotCar Platform (p. 1) describes fisheye cameras, and the paper says the dataset is available for download. But the Introduction (p. 1) does not assess how many public fisheye datasets exist, and searches for semantic-segmentation annotations found no passage saying that none provide them. (LLM, conf=0.72)
- Not checked — no full text of the sourcecites [9] source
- Not matchedcites [37]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Could not verify
The cited sources that could be checked couldn’t confirm or dispute this claim.
In the paper
“In the recent EuroCity persons dataset [5], the authors argued that any anonymization measure will introduce a bias.Thus they released their dataset with original data and a license agreement which enforces the user to strictly adhere to GDPR.We will follow a similar approach.”
§ WoodScape: A multi-task, multi-camera fisheye dataset for autonomous driving > GDPR challenges:
What the sources say
- Could not verifycites [5] sourceExperimental — accuracy not yet measured
Split into 2 atoms — 0 confirmed, 0 partially supported, 0 disputed, 0 misattributed, 2 unverifiable. Expand the claim to see per-atom verdicts.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Could not verify
The cited sources that could be checked couldn’t confirm or dispute this claim.
In the paper
“Bojarski et al. demonstrated end-to-end learning [2] for steering and recently it was applied to fisheye cameras [54].”
§ WoodScape: A multi-task, multi-camera fisheye dataset for autonomous driving > 4.9. End-to-End Steering/Braking
What the sources say
- Not checked — no full text of the sourcecites [2] source
- Could not verifycites [54] sourceExperimental — accuracy not yet measured
The Abstract and Introduction discuss semantic segmentation, but neither addresses end-to-end steering with fisheye cameras. Searches for fisheye steering and Bojarski found no relevant passage in our copy. (LLM, conf=0.76)
Strength:boosted· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The cited sources that could be checked couldn’t confirm or dispute this claim.
In the paper
“There are several popular synthetic datasets like SYNTHIA [44] and CARLA [13].”
§ WoodScape: A multi-task, multi-camera fisheye dataset for autonomous driving > 4.8. Synthetic Data Domain Transfer
What the sources say
- Could not verifycites [44] sourceExperimental — accuracy not yet measured
The citation is attached to SYNTHIA, but the passages from “Selecting Representative Data Sets” (page unavailable) discuss representative-set selection and SMOTE synthetic instances, not SYNTHIA. Searches for SYNTHIA and CARLA found no passage identifying either dataset or addressing its popularity. (LLM, conf=0.76)
- Not matchedcites [13]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Could not verify
The cited sources that could be checked couldn’t confirm or dispute this claim.
In the paper
“Semantic segmentation networks for autonomous driving [47] have been successfully trained directly on fisheye images in [12,45].Due to absence of fisheye datasets, they make use of artificially warped images of Cityscapes for training and testing was performed on fisheye images.However, the artificial images cannot increase the originally captured FOV.”
§ WoodScape: A multi-task, multi-camera fisheye dataset for autonomous driving > 4.1. Semantic Segmentation
What the sources say
- Could not verifycites [47] sourceExperimental — accuracy not yet measured
Split into 2 atoms — 0 confirmed, 0 partially supported, 0 disputed, 0 misattributed, 2 unverifiable. Expand the claim to see per-atom verdicts.
- Not checked — no full text of the sourcecites [12] source
- Not checked — no full text of the sourcecites [45] source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Rests on an assumption: Artificially warped images cannot increase the originally captured field of view.
- Could not verify
The cited sources that could be checked couldn’t confirm or dispute this claim.
In the paper
“This has been explored in [49,57,48] for narrow angle cameras.”
§ WoodScape: A multi-task, multi-camera fisheye dataset for autonomous driving > 4.6. Motion Segmentation
What the sources say
- Not matchedcites [49]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [57]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Could not verifycites [48] sourceExperimental — accuracy not yet measured
The Introduction (p. 1) describes semantic segmentation for automated driving, and the Auxiliary Task Learning section (p. 3) illustrates optical flow as a possible task. Searches for motion segmentation and narrow-angle cameras found no passage saying this paper explored motion segmentation with such cameras. (LLM, conf=0.73)
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
The cited sources that could be checked couldn’t confirm or dispute this claim.
In the paper
“The dataset supports correct hypothesis evaluation [55], therefore multiple splits are provided (5 in total).”
§ WoodScape: A multi-task, multi-camera fisheye dataset for autonomous driving > Data splitting and class balancing:
What the sources say
- Could not verifycites [55] sourceExperimental — accuracy not yet measured
The section on large annotated datasets discusses CamVid, Cityscapes and other datasets, but not WoodScape. The benchmarking section describes an evaluation on CamVid; neither it nor the other passages found addresses WoodScape’s support for hypothesis evaluation or its five splits (searched: WoodScape, hypothesis evaluation, dataset splits). (LLM, conf=0.76)
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- Could not verify
The cited sources that could be checked couldn’t confirm or dispute this claim.
In the paper
“In general, 3D IoU [18] is used to evaluate 3D bounding box predictions”
§ WoodScape: A multi-task, multi-camera fisheye dataset for autonomous driving > 4.4. 3D Bounding Box Detection
What the sources say
- Could not verifycites [18] sourceExperimental — accuracy not yet measured
Section 4.6, “A note on accuracy” (p. 11), discusses detection and localisation accuracy for automated parking, but does not identify 3D IoU as an evaluation metric. Searches for 3D IoU, intersection over union, and bounding-box evaluation found no passage addressing the claim. (LLM, conf=0.78)
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
- Could not verify
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“In our experience, higher orders provide no additional accuracy.”
§ WoodScape: A multi-task, multi-camera fisheye dataset for autonomous driving > 2.1. Fisheye Camera Models
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“In WoodScape, we provide model parameters for a more generic fisheye intrinsic calibration that is independent of any specific projection model, and does not require the added step of distortion correction.”
§ WoodScape: A multi-task, multi-camera fisheye dataset for autonomous driving > 2.1. Fisheye Camera Models
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Introduction of a novel soiling detection task and release of first dataset of its kind.”
§ WoodScape: A multi-task, multi-camera fisheye dataset for autonomous driving > 1. Introduction
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It contains four cameras covering 360 ° accompanied by a HD laser scanner, IMU and GNSS.”
- Also stated in: § 1. Introduction · plain
“WoodScape is a comprehensive dataset for 360 ° sensing around a vehicle using the four fisheye cameras shown in Figure 2.”
§ WoodScape: A multi-task, multi-camera fisheye dataset for autonomous driving > Abstract
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Also stated in: § 1. Introduction · plain
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Four-camera nine-task dataset designed to encourage unified multi-task and multi-camera models.”
§ WoodScape: A multi-task, multi-camera fisheye dataset for autonomous driving > 1. Introduction
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The plot indicates that the difference to the original fourth order polynomial is about four pixels for UCM and one pixel for eUCM for low incident angles.For larger incident angles, these models are less precise.”
§ WoodScape: A multi-task, multi-camera fisheye dataset for autonomous driving > 2.1. Fisheye Camera Models
Strength:hedged· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“We introduce WoodScape, the first fisheye image dataset dedicated to autonomous driving.”
§ WoodScape: A multi-task, multi-camera fisheye dataset for autonomous driving > Abstract
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. The paper may give no reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“While the majority of data was obtained from saloon vehicles there is a significant subset from a sports utility vehicle ensuring a strong mix in sensor mechanical configurations.”
§ WoodScape: A multi-task, multi-camera fisheye dataset for autonomous driving > 3.2. Dataset Acquisition
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“More than one third of our dataset is recorded in Europe and is therefore GDPR sensitive due to visible faces of pedestrians and license plates.”
§ WoodScape: A multi-task, multi-camera fisheye dataset for autonomous driving > GDPR challenges:
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Driving scenarios are divided across the highway, urban driving and parking use cases.”
§ WoodScape: A multi-task, multi-camera fisheye dataset for autonomous driving > 3.2. Dataset Acquisition
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The video sequences can also be used for Visual SLAM where we focus on relocalization of a mapped trajectory and the metric is same as VO.”
§ WoodScape: A multi-task, multi-camera fisheye dataset for autonomous driving > 4.7. Visual Odometry/SLAM
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Even though this comparison uses 3D-IoU, we achieve similar performance for average precision (3D-IoU), with better angle orientation similarity (AOS) and much faster computation time.”
§ WoodScape: A multi-task, multi-camera fisheye dataset for autonomous driving > 4.4. 3D Bounding Box Detection
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The dataset also provides a set of synthetic data using accurate models of the real cameras, enabling investigations of additional tasks.”
§ WoodScape: A multi-task, multi-camera fisheye dataset for autonomous driving > 3.2. Dataset Acquisition
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Thus we have added end-to-end steering and braking tasks to encourage modular end-to-end architectures and to explore optimized perception for the control task.”
§ WoodScape: A multi-task, multi-camera fisheye dataset for autonomous driving > 4.9. End-to-End Steering/Braking
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We provide 50 video sequences comprising of over 100k frames with ground truth.”
§ WoodScape: A multi-task, multi-camera fisheye dataset for autonomous driving > 4.7. Visual Odometry/SLAM
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Images are provided at 1MPx 24-bit resolution and videos are uncompressed at 30fps ranging in duration from 30s to 120s.”
§ WoodScape: A multi-task, multi-camera fisheye dataset for autonomous driving > 3.2. Dataset Acquisition
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“• 4x 1MPx RGB fisheye cameras (190 • horizontal FOV) • 1x LiDAR rotating at 20Hz (Velodyne HDL-64E) • 1x GNSS/IMU (NovAtel Propak6 & SPAN-IGM-A1) • 1x GNSS Positioning with SPS (Garmin 18x) • Odometry signals from the vehicle bus.”
§ WoodScape: A multi-task, multi-camera fisheye dataset for autonomous driving > 3.2. Dataset Acquisition
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Intrinsic and extrinsic calibrations are provided for all sensors as well as timestamp files to allow synchronization of the data.”
§ WoodScape: A multi-task, multi-camera fisheye dataset for autonomous driving > 3.2. Dataset Acquisition
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“| Segmentation | Classes Frames | 8 400 | 30 5k | 66 25k | -- | 25 140k | 40 5.7k | 40 10k |”
§ WoodScape: A multi-task, multi-camera fisheye dataset for autonomous driving > 5. Conclusions
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“We achieve a mAP score of 31 which is significantly less than the accuracy achieved in other datasets.”
§ WoodScape: A multi-task, multi-camera fisheye dataset for autonomous driving > 4.2. 2D Bounding Box Detection
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“We also provide previous and next images for exploring multi-stream models like MOD-Net [49].”
§ WoodScape: A multi-task, multi-camera fisheye dataset for autonomous driving > 4.6. Motion Segmentation
What the sources say
- Not checked — no full text of the sourcecites [49] source
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In addition to providing fisheye data, we provide data for many more tasks than is typical (nine in total), providing completely novel tasks such as soiled lens detection.”
§ WoodScape: A multi-task, multi-camera fisheye dataset for autonomous driving > 3.2. Dataset Acquisition
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“3D box annotation is provided for 10k frames with 3 classes namely 'pedestrian', 'vehicle' and 'cyclist'.”
§ WoodScape: A multi-task, multi-camera fisheye dataset for autonomous driving > 4.4. 3D Bounding Box Detection
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We provide more than 100k images of all four cameras (totaling 400k) using ground truth provided by LiDAR.”
§ WoodScape: A multi-task, multi-camera fisheye dataset for autonomous driving > 4.5. Monocular Depth Estimation
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“| 2D Bounding Box 1 | Classes Frames | 3 15k | -- | -- | -- | -- | 10 5.7k | 7 10k |”
§ WoodScape: A multi-task, multi-camera fisheye dataset for autonomous driving > 5. Conclusions
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
In the paper
“Fisheye is particularly beneficial to automotive low speed manoeuvring scenarios such as parking [21] where accurate full coverage near field sensing can be achieved with just four cameras.”
§ WoodScape: A multi-task, multi-camera fisheye dataset for autonomous driving > 3.1. High-Level Goals
What the sources say
- Not matchedcites [21]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Not matched
A claim is checked only against the cited paper’s full text, never its abstract, and none is available for its sources.
In the paper
“However, spherical models do not provide an accurate fit for fisheye lenses and it is an open problem.”
§ WoodScape: A multi-task, multi-camera fisheye dataset for autonomous driving > 2.2. Image Undistortion vs. Model Adaptation
What the sources say
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Because of fundamental limitations of undistortion, an alternate approach of adapting the algorithm incorporating fisheye model discussed in previous section could be an optimal solution.”
§ WoodScape: A multi-task, multi-camera fisheye dataset for autonomous driving > 2.2. Image Undistortion vs. Model Adaptation
Tests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may be too vague to check against evidence.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“This advantage comes with some drawbacks in the significantly more complex projection geometry that fisheye cameras exhibit.That is, images from fisheye cameras display severe distortion.”
§ WoodScape: A multi-task, multi-camera fisheye dataset for autonomous driving > 2. Overview of Fisheye Camera Projections
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Firstly, the FOV is greater than 180 • , hence there are rays incident from behind the camera and it is not possible to establish a complete mapping to a rectilinear viewport.This leads to a loss of FOV, this is seen via the missing yellow pillars in the corrected image.”
§ WoodScape: A multi-task, multi-camera fisheye dataset for autonomous driving > 2.2. Image Undistortion vs. Model Adaptation
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Given their extremely wide field of view, they can observe the full surrounding of a vehicle with a minimal number of sensors, with just four cameras typi- cally being required for full 360 • coverage (Figure 2).”
§ WoodScape: A multi-task, multi-camera fisheye dataset for autonomous driving > 2. Overview of Fisheye Camera Projections
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Standard computer vision models do not generalize easily to fisheye cameras because of large non-linear distortion.For example, translation invariance is lost for a standard convolutional neural net (CNN).”
§ WoodScape: A multi-task, multi-camera fisheye dataset for autonomous driving > 2.2. Image Undistortion vs. Model Adaptation
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Existing datasets are primarily designed to facilitate task-specific learning and they don't provide simultaneous annotation for all the tasks.”
§ WoodScape: A multi-task, multi-camera fisheye dataset for autonomous driving > 3.1. High-Level Goals
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes? Based: unclearIt may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Full control of the splitting mechanism is provided allowing for each class to be represented equally within each split (i.e.stratified sampling).”
§ WoodScape: A multi-task, multi-camera fisheye dataset for autonomous driving > Data splitting and class balancing:
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Unlike the front camera which is behind the windshield, the surround view cameras are usually directly exposed to the adverse environmental conditions, and thus prone to becoming soiled or water drops forming on the lens.”
§ WoodScape: A multi-task, multi-camera fisheye dataset for autonomous driving > 4.3. Camera Soiling Detection
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The classes to detect are {opaque, transparent}.Typically, opaque soiling arises from mud and dust (Figure 8 right image), and transparent soiling arises from water and ice (Figure 8 left image).However, in practice it is common to see water producing”
§ WoodScape: A multi-task, multi-camera fisheye dataset for autonomous driving > 4.3. Camera Soiling Detection
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Rests on an assumption: Opaque soiling typically arises from mud and dust, and transparent soiling from water and ice.
- Rests on an assumption: Water can produce 'opaque' regions in the camera image.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“High-quality data is ensured via quality checks at all stages of the data collection process.Annotation data undergoes a rigorous quality assurance by highly skilled reviewers.”
§ WoodScape: A multi-task, multi-camera fisheye dataset for autonomous driving > 3.2. Dataset Acquisition
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We observe that it is necessary to incorporate the fisheye geometry explicitly, which is an open research problem.”
§ WoodScape: A multi-task, multi-camera fisheye dataset for autonomous driving > 4.2. 2D Bounding Box Detection
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Collecting frames at a frame rate of 30fps, particularly at low speeds, ultimately leads to significant image overlap, therefore, having an effective sampling strategy to distill the dataset is critical.”
§ WoodScape: A multi-task, multi-camera fisheye dataset for autonomous driving > Sampling strategy:
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“The task of soiling detection was to our best knowledge first defined in [56].”
§ WoodScape: A multi-task, multi-camera fisheye dataset for autonomous driving > 4.3. Camera Soiling Detection
What the sources say
- Not checked — no full text of the sourcecites [56] source
Strength:hedged· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“However, at the same time, it was shown that careful inspection of the training sets for outliers improves the robustness of deep neural networks [36], especially with regards to the adversarial examples.”
§ WoodScape: A multi-task, multi-camera fisheye dataset for autonomous driving > 3.3. Dataset Design
What the sources say
- Not checked — no full text of the sourcecites [36] source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
In the paper
“Visual Odometry (VO) is necessary for creating a map from the objects detected [38].”
§ WoodScape: A multi-task, multi-camera fisheye dataset for autonomous driving > 4.7. Visual Odometry/SLAM
What the sources say
- Not checked — no full text of the sourcecites [38] source
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
The paper relies on this without support.
Surround view systems have at least four cameras rigidly connected to the body of the car.
“Surround view systems have at least four cameras rigidly connected to the body of the car.”
The paper relies on this without support.
Artificially warped images cannot increase the originally captured field of view.
“However, the artificial images cannot increase the originally captured FOV.”
The paper relies on this without support.
Opaque soiling typically arises from mud and dust, and transparent soiling from water and ice.
“Typically, opaque soiling arises from mud and dust (Figure 8 right image), and transparent soiling arises from water and ice (Figure 8 left image).”
The paper relies on this without support.
Water can produce 'opaque' regions in the camera image.
“However, in practice it is common to see water producing”
The paper relies on this without support.
3D IoU has drawbacks, especially for rotated objects.
“In general, 3D IoU [18] is used to evaluate 3D bounding box predictions, but there are drawbacks, especially for rotated objects.”
The paper relies on this without support.
Two boxes can reach a good 3D IoU score while overlapping in total with an opposite heading.
“Two boxes can reach a good 3D IoU score, while overlapping in total with an opposite heading.”
The paper relies on this without support.
Exact calculation in 3D space is time-consuming.
“Additionally, an exact calculation in 3D space is a time consuming task.”
The paper relies on this without support.
Motion is a strong cue in automotive due to ego-motion of cameras and dynamic objects are critical interacting agents.
“In automotive, motion is a strong cue due to ego-motion of the cameras on the moving vehicle and dynamic objects around the vehicle are the critical interacting agents.”
The paper relies on this without support.
Synthetic data is crucial for autonomous driving.
“Synthetic data is crucial for autonomous driving for many reasons.”
The paper relies on this without support.
There are legal restrictions like recording videos of a child.
“Secondly, there are legal restrictions like recording videos of a child.”
The paper relies on this without support.
Synthetic data is the only way to obtain dense depth and optical flow annotation.
“Finally, synthetic data is the only way to obtain dense depth and optical flow annotation.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“Other works explored image restoration using inpainting or generative methods [3,4], though such approaches carry risks of hallucinated visual cues that may mislead the perception system.”
From AI-Based Image Quality and Lens Defect Analysis in Autonomous Driving: A Framework with U-Net-Based Soiling Detection (2025) · cites this paper as [4]
Experimental — accuracy not yet measured
Citations
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · cited 3×
“7 Motion Segmentation MODNet [49] IoU 45 Visual Odometry ResNet50 [20] Translation (<5mm) 51 Rotation (<0.”
BackgroundMethodAdam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · cited 1×
“We fine-tune their model for our dataset by training with categorical cross entropy loss and Adam [29] optimizer.”
MethodFaster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 73,472
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1506.01497 · Cited by 70,812 · cited 3×
“4 2D Bounding Box Faster R-CNN [43] mAP (IoU>0.”
BackgroundMethodImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333 · cited 1×
“We initialize the network with ImageNet [11] pre-trained weights.”
MethodThe pascal visual object classes (VOC) challenge
Everingham, M., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2010) · International Journal of Computer Vision · doi:10.1007/s11263-009-0275-4 · Cited by 19,599
Are we ready for autonomous driving? The KITTI vision benchmark suite
Geiger, A., Lenz, P., & Urtasun, R. (2012) · 2012 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2012.6248074 · Cited by 14,897 · cited 12×
“Performance of 2D object detection is reported in terms of mean average precision (mAP) when IoU≥ 0.5 between predicted and ground truth bounding boxes.”
BackgroundMethodInfluentialThe Cityscapes Dataset for Semantic Urban Scene Understanding
Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., & Schiele, B. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.350 · Cited by 12,433 · cited 4×
“Task/Info Quantity KITTI [17] Cityscapes [10] Mapillary [39] nuScenes [6] ApolloScape [24] BDD100k [59] WoodScape Ours”
BackgroundMethodInfluentialA Style-Based Generator Architecture for Generative Adversarial Networks
Karras, T., Laine, S., & Aila, T. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00453 · Cited by 11,714 · cited 1×
“the desired precision. Another, more appropriate, approach is to exchange these regions by some automatically generated proposals. For example, exchanging all faces for automatically generated ones2 [25]. Such anonymized data will not violate GDPR rules as the original person is no longer identifiable. We aim to ensure…”
BackgroundVision meets robotics: The KITTI dataset
Geiger, A., Lenz, P., Stiller, C., & Urtasun, R. (2013) · The International Journal of Robotics Research · doi:10.1177/0278364913491297 · Cited by 9,913 · cited 3×
“Task/Info Quantity KITTI [17] Cityscapes [10] Mapillary [39] nuScenes [6] ApolloScape [24] BDD100k [59] WoodScape Ours”
Background
Flagged first, then by citations
9 of the 78 references couldn’t be matched to a known paper.
Research works printed but not linked: 2
- kumar, 2018, Near-field depth estimation using monocular fisheye camera: A semi-supervised learning approach using sparse LiDAR data, CVPR Workshop
- caruso, 2015, Largescale direct SLAM for omnidirectional cameras, Proceedinsg of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 141
Cited by
Sorted by citations
Items 201–300 of 300
A Data-Centric Approach for Object Recognition in Hemispherical Camera Images
Balgabekova, Z., Alaran, M., & Varol, H. A. (2023) · Annual Conference of the IEEE Industrial Electronics Society (IECON) · doi:10.1109/iecon51785.2023.10311869 · Cited by 2
“However, few public datasets exist for a thorough assessment of CV techniques on fisheye images [20].”
BackgroundMethodInfluentialSemantic Segmentation Using Transfer Learning on Fisheye Images
Paul, S., Patterson, Z., & Bouguila, N. (2023) · International Conference on Machine Learning and Applications · doi:10.1109/icmla58977.2023.00068 · Cited by 2
“We investigate six different transfer learning pipelines for fisheye segmentation using the WoodScape dataset [1].”
BackgroundMethodInfluentialRaidaR: A Rich Annotated Image Dataset of Rainy Street ScenesPreprint
Jin, J., Fatemi, A., Lira, W., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.04606 · Cited by 2
“There are many available datasets to train autonomous driving systems [11, 55, 54, 44, 42, 16].”
BackgroundAutomotive Target Models for Point Cloud Sensors
Kaulbersch, H. (2022) · doi:10.53846/goediss-9061 · Cited by 2
Measuring Natural Scenes SFR of Automotive Fisheye Cameras
Jakab, D., Grua, E. M., Deegan, B. M., et al. (2024) · Electronic Imaging · doi:10.2352/ei.2024.36.17.avm-109 · Cited by 1
Self-citationLi, H., Li, Y., Wang, H., Zeng, J., Xu, H., Cai, P., Chen, L., Yan, J., Feng, X., Xiong, L., et al. (2024) · Scientia Sinica Informationis · doi:10.1360/ssi-2023-0313 · Cited by 30
Zero-BEV: Zero-shot Projection of Any First-Person Modality to BEV Maps
Monaci, G., Antsfeld, L., Chidlovskii, B., & Wolf, C. (2024) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv62453.2024.00161 · Cited by 1
“Thus several works, especially in autonomous driving, use a unified framework to conduct multiple tasks simultaneously [18, 36, 55].”
BackgroundSensor Equivariance: A Framework for Semantic Segmentation with Diverse Camera Models
Reichert, H., Hetzel, M., Hubert, A., Doll, K., & Sick, B. (2024) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00132 · Cited by 1
“In an experiment based on the Wood-Scapes dataset [23], the authors show that their approach is capable of generalizing over the fish-eye cameras used in the WoodScape setup for self-supervised distance estimation.”
MethodSoiling Builds up Gradually : Soiling Semantic Segmentation
Kim, T., Lee, W., & Hwang, Y. (2024) · International Conference on Control, Automation and Systems (ICCAS) · doi:10.23919/iccas63016.2024.10773253 · Cited by 1
ViewpointDepth: A New Dataset for Monocular Depth Estimation Under Viewpoint Shifts
Pjetri, A., Caprasecca, S., Taccari, L., et al. (2025) · IEEE Intelligent Vehicles Symposium · doi:10.1109/iv64158.2025.11097590 · Cited by 2
Wenke, E., Yuan, C., Li, L., et al. (2025) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra55743.2025.11128609 · Cited by 1
“For example, nuScenes [3] employs six cameras to achieve a 360-degree visual coverage, whereas WoodScape [29] requires four fisheye cameras, and KITTI-360 [30] manages with only two.”
InfluentialAdapting CNNs for Fisheye Cameras without Retraining
Griffiths, R., & Dansereau, D. G. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11227667 · Cited by 1
“A linear planar sampling k of scale s is calculated to be tangential to the point p c at the centre of the original grid, from which new sample points can be calculated Here ˆ p i is the new point in space at position i in the kernel.”
InfluentialPARTNER: Level Up the Polar Representation for 3D Object Detection
Nie, M., Wang, C., Xu, H., & Zhang, L. (2026) · International Journal of Computer Vision · doi:10.1007/s11263-026-02735-0 · Cited by 1
Distortion-Aware Depth Self-Updating for Self-Supervised Fisheye Monocular Depth Estimation
Xu, Y., & Dong, Q. (2026) · IEEE Transactions on Image Processing · doi:10.1109/tip.2026.3661813 · Cited by 2
Evaluating the Impact of Weather-Induced Sensor Occlusion on BEVFusion for 3D Object Detection
Kumar, S., Brophy, T., Grua, E. M., et al. (2025) · IEEE International Conference on Vehicular Electronics and Safety (ICVES) · doi:10.1109/icves65691.2025.11376003 · Cited by 1
“Camera occlusions are simulated using soiling masks from the Woodscape dataset [12] to replicate real-world lens obstructions.”
Self-citationToward Oriented Multi-Object Tracking for Fisheye Images: Dataset and Framework
Yang, J., Lin, C., Nie, L., Tang, Y., & Zhao, Y. (2026) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2026.3666162 · Cited by 1
“However, most perception benchmarks or research on fisheye images are about detection, such as [20], [41], [42], and [43].”
RICO: Two Realistic Benchmarks and an In-Depth Analysis for Incremental Learning in Object Detection
Neuwirth-Trapp, M., Bieshaar, M., Paudel, D. P., & Goo, L. V. (2025) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw69036.2025.00543 · Cited by 1
Towards Safe Visual Navigation of a Wheelchair Using Landmark Detection
Sevastopoulos, C., Zadeh, M. Z., Theofanidis, M., Acharya, S., Patel, N. H., & Makedon, F. S. (2023) · Technologies · doi:10.3390/technologies11030064 · Cited by 1
“Ultra wide-lens images, such as obtained from fisheye cameras, have been used in people detection methods [14], robot traversability estimation [15], SLAM [16], pedestrian/vehicle detection and tracking [17], and autonomous driving [18].”
MethodJurdi, R. E., Sekkat, A. R., Dupuis, Y., Vasseur, P., & Honeiné, P. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-16627-9 · Cited by 2
“In this paper, we used two well-known datasets, WoodScape [36] and its synthetic version the SynWoodScape [30].”
One-Epoch Training for Object Detection in Fisheye Images
Chen, Y.-H. (2023) · ACM Multimedia Asia (MMAsia) · doi:10.1145/3595916.3628349 · Cited by 1
Artificial Fish-Eye Image Augmentation Approach for CNN Based Object Detection
Broks, M., Teličko, J., & Jakovičs, A. (2024) · IEEE Workshop on Advances in Information, Electronic and Electrical Engineering (AIEEE) · doi:10.1109/aieee62837.2024.10586656 · Cited by 1
“The third testing split was obtained by filtering out all images containing class ’person’ in the WoodScape dataset [10].”
MethodGiraud, R., & Clément, M. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-78347-0_5 · Cited by 1
GCBAM-UNet: Sun Glare Segmentation Using Convolutional Block Attention Module
Zrira, N., Jimi, A., Nardo, M. D., Elafi, I., Gallab, M., & Ouazzani, R. C. E. (2024) · Applied System Innovation · doi:10.3390/asi7060128 · Cited by 3
Xinlei, 王. W., Chenxu, 廖. L., Shuo, 王. W., & Ruilin, 肖. X. (2025) · Laser & Optoelectronics Progress · doi:10.3788/lop241236 · Cited by 1
Eberhardt, T. D., Brühl, T., Schwager, R., Sohn, T. S., & Stork, W. (2024) · IEEE International Conference on Vehicular Electronics and Safety (ICVES) · doi:10.1109/icves61986.2024.10927915 · Cited by 1
“The creation of a comprehensive dataset featuring both opaque and transparent soiling variants, that is part of the publicly available WoodScape dataset [5], supports this development.”
A Lightweight and Data-Centric Framework for Real-Time Object Detection in Fisheye Camera
Vinh, A. T., Vinh, N. M., Si, N. L., Quoc, D. D., Khánh, D. T., Hoang, N. N., Do, T., Ngo, T. D., Le, D.-D., & Satoh, S. (2025) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw69036.2025.00576 · Cited by 1
“WoodScape [30] paved the way with 8,234 self-driving images, followed by Fisheye8K [13] and VIPCup [15], which extend dataset scale and realism through street-level omnidirectional coverage.”
Expandable Spherical Projection and Feature Fusion Methods for Object Detection from Fisheye Images
Kim, S., & Park, S.-Y. (2021) · doi:10.23919/mva51890.2021.9511379 · Cited by 1
Kuznetsov, V. I., & Yudin, D. A. (2022) · Optical Memory and Neural Networks · doi:10.3103/s1060992x22030043 · Cited by 1
“Therefore, it was decided to develop our own dataset by partially combining images from the TapmerDetection [2], ACDC [40], DrivingStereo (“Different weathers” sample) [39] and Woodscape (“Soiling Detection” sample) [38] datasets, for which radial distortion was eliminated and the images were rectified.”
MethodAttention Augmented Pedestrian Detection on Fisheye Cameras
Wang, A., Li, Y., & Zheng, R. (2023) · IEEE Information Technology, Networking, Electronic and Automation Control Conference (ITNEC) · doi:10.1109/itnec56291.2023.10082540 · Cited by 1
“1) Datasets: We carry out a range of experiments on PASCAL VOC 2007, PASCAL VOC 2012 [9] and WoodScape [3] dataset.”
BackgroundMethodInfluentialUniversal Semantic Segmentation for Fisheye Urban Driving ImagesPreprint
Ye, Y., Yang, K., Xiang, K., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.03736 · Cited by 1
“WoodScape [16], a multi-task, multi-camera fisheye dataset for autonomous driving, and OmniScape [17], a synthetic dataset, could meet our requirement, but they have not been released yet.”
Distortion-Adaptive Grape Bunch Counting for Omnidirectional ImagesPreprint
Akai, R., Utsumi, Y., Miwa, Y., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2008.12511 · Cited by 1
Capturing Omni-Range Context for Omnidirectional SegmentationPreprint
Yang, K., Zhang, J., Reiß, S., Hu, X., & Stiefelhagen, R. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.05687 · Cited by 1
“The labeling process is extremely time- and labor-intensive, thus, only few surroundview datasets [52, 63, 76] are publicly accessible and none capture as diverse scenes as contemporary pinhole datasets [38, 58] do.”
BackgroundO’g’li, A. (2025) · American Journal of Mechanics and Applications · doi:10.11648/j.ajma.20251204.14 · Cited by 1
Multi-Task Network Pruning and Embedded Optimization for Real-time Deployment in ADASPreprint
Dellinger, F., Boulay, T., Barrenechea, D. M., El-Hachimi, S., Leang, I., & Bürger, F. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.07831 · Cited by 6
“We run our experiments on the Woodscape autonomous driving dataset [34] using a split of 80/10/10 for train/val/test and a reduced number of classes: road, lane marking, curb, vehicle, two wheeler and person.”
MethodRobust Keypoint Detection and Matching on Fisheye Images by Self-Supervised Learning
Tian, W., Cai, P., Wen, Y., et al. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/4024774
Anderson, A., Olafarson, E. J., Andersen, P., & Noori, N. S. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-21441-7_14
Identity Generation with Deep Generative Models
Letizia, N. A. (2023) · Računalniška obdelava slik in njena uporaba v Sloveniji (ROSUS) · doi:10.18690/um.feri.4.2023.3
Detekcija puščic pri klasičnem pikadu
Zgonc, M., & Batagelj, B. (2023) · Računalniška obdelava slik in njena uporaba v Sloveniji (ROSUS) · doi:10.18690/um.feri.4.2023.11
A Data-Centric Approach for Object Recognition in Hemispherical Camera ImagesPreprint
Balgabekova, Z., Alaran, M., & Varol, H. A. (2023) · doi:10.36227/techrxiv.23016185
A Data-Centric Approach for Object Recognition in Hemispherical Camera ImagesPreprint
Balgabekova, Z., Alaran, M., & Varol, H. A. (2023) · doi:10.36227/techrxiv.23016185.v1 · Cited by 1
Analysis of Faster R-CNN network prediction in the presence of lens occlusion and video compressionPreprint
Baris, G., Li, B., Chan, P. H., Avizzano, C. A., & Donzella, V. (2023) · doi:10.36227/techrxiv.23047412.v1
Analysis of Faster R-CNN network prediction in the presence of lens occlusion and video compressionPreprint
Baris, G., Li, B., Chan, P. H., Avizzano, C. A., & Donzella, V. (2023) · doi:10.36227/techrxiv.23047412
Domain Adaptation from Visible-Light to FIR with Reliable Pseudo Labels
Tanimoto, J., Kyutoku, H., Doman, K., et al. (2023) · doi:10.23919/mva57639.2023.10216102
“Large-scale in-vehicle camera image datasets captured by visible-light (VL) cameras have been developed[1, 2, 3, 4, 5, 6], which enables deep learning models to achieve highly accurate detection in the VL domain.”
BackgroundSu, X. Y., Wang, X., Li, H., et al. (2023) · Recent Patents on Mechanical Engineering · doi:10.2174/0122127976268634230929182355
Stereo Visual Mesh for Generating Sparse Semantic Maps at High Frame Rates
Biddulph, A., Houliston, T., Mendes, A. S., et al. (2023) · Lecture notes in computer science · doi:10.1007/978-981-99-8076-5_12
Semantic Segmentation for Edge Devices—Survey and an End-to-End Readiness on the Target Platform
Gujjula, V. T., Joshi, N., Sambi, S., Awasthi, A., & Singh, J. K. (2023) · Lecture notes in electrical engineering · doi:10.1007/978-981-99-5974-7_42
Research on Semantic Segmentation of Fish-Eye Images for Autonomous Driving
Huang, H., Tian, X., & Tian, W. (2023) · International Journal of Robotics and Automation Technology · doi:10.31875/2409-9694.2023.10.13
Ni, Y.-S., Tsai, C., Lin, J.-S., et al. (2023) · ACM Multimedia Asia (MMAsia) · doi:10.1145/3595916.3628352
“To boost the recognition of bicycles and pedestrians, they incorporate the WoodScape dataset [14] into their training data, which provides rich information about these objects.”
MethodAutoshuttle: A Novel Dataset for Advancing Autonomous Driving in Shuttle-Specific Environments
Zhou, L., Salaar, H., Schmidt, M., Dehghani, A., & Arbeiter, G. (2024) · IEEE World Symposium on Applied Machine Intelligence and Informatics (SAMI) · doi:10.1109/sami60510.2024.10432901
Streaming Object Detection on Fisheye Cameras for Automatic Parking
Yan, Y., Cheng, L., Li, Y., Tuo, X., Huang, B., & Zhu, Y. (2024) · Lecture notes in electrical engineering · doi:10.1007/978-981-97-0252-7_6
European Common Data Management Platform Definition for Railway AI Function Development
Labayen, M., de Eribe, D. O., Aramburu, A., Nieto, M., & Aginako, N. (2024) · Transportation Development Research · doi:10.55121/tdr.v2i1.143
“…benchmarking purposes (e.g., KITTI [6] , Virtual KITTI [7] , nuScenes [8] , Apollo [9] ), to gain prestige (e.g., Audi A2D2 [10] , Waymo [11] , Ford [12] , Lyft5 [13] ), or pioneering in specific application domains (e.g., Woodscape [14] on fish - eye segmentation, DMD [15] on driver monitoring).”
BackgroundHierarchical Competition Learning for Pairwise Wheel Grounding Points Estimation
Zhang, Y., & Yuan, Z. (2024) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2024.3401245
“We use fisheye cameras [11], [12] to obtain a larger field of view and expect to detect surrounding vehicles and their grounding points.”
MethodA universal method for extrinsic calibration of LiDAR and camera
Guo, H., Liu, J., Riaz, M., et al. (2024) · International Conference on Image, Signal Processing and Pattern Recognition (ISPP) · doi:10.1117/12.3034105
An efficient framework for creating depth annotated image datasets with camera and Lidar fusion
Liu, J., Guo, H., Wang, Y., et al. (2024) · International Conference on Remote Sensing, Mapping, and Image Processing (RSMIP) · doi:10.1117/12.3029626
“Currently, mainstream depth camera datasets include the KITTI dataset [2] , DrivingStereo dataset [3] , WoodScape dataset [8] , and nuscenes dataset [7] .”
Comprehensive Teacher-based Multi-task Model Train Using a Small Amount of Data
Zhu, Y., & Xiao, N. (2024) · doi:10.1109/ijcnn60899.2024.10651103
“We chose to validate our model on three autonomous driving datasets: BDD100k [14], SHIFT [18], and WoodScape [17].”
MethodRan, L., Hu, Z., Lu, X., & Wu, Z. (2024) · SAE technical papers on CD-ROM/SAE technical paper series · doi:10.4271/2024-01-7032
Cost-effective annotation of fisheye images for object detection
Zhang, K., Elalailyi, A., Perfetti, L., & Fassi, F. (2024) · The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-archives-xlviii-2-w8-2024-491-2024
Synthetic Soiling Augmentation Using the Diffusion Model for Automotive Camera System
Heo, J., Kim, T., & Hwang, Y. (2024) · International Conference on Platform Technology and Service · doi:10.1109/platcon63925.2024.10830729
“Using Woodscape dataset [25], which includes multiple foreign object images in various driving environments, we train a generative model using soiling data from a single view.”
3D Object Detection based on Surround-View Fisheye Cameras
Huang, H., & Tian, W. (2024) · CAA International Conference on Vehicular Control and Intelligence (CVCI) · doi:10.1109/cvci63518.2024.10830142
“Woodscape [5] is the first surround view fisheye image dataset for autonomous driving scenarios, offering perspectives from four fisheye cameras along with multi-task annotations.”
Wang, M., Xu, Z., Kou, K., et al. (2024) · International Conference on Communication Networks and Machine Learning (CNML) · doi:10.1145/3701047.3701073
“WoodScape[12] is the first publicly available comprehensive omnidirectional fisheye dataset.”
Field of View Invariant Object Recognition Using Non-Linear Transformation Augmentation
Alaran, M., Balgabekova, Z., & Varol, H. A. (2024) · Annual Conference of the IEEE Industrial Electronics Society (IECON) · doi:10.1109/iecon55916.2024.10905287
“Some of these include SynWoodScape [19], FishEye8K dataset [20], WoodScape [21], KITTI-360 [22], Oxford RobotCar [23] and OmniScape [24].”
Li, J., Wang, H., Zhang, S., Meng, C. L., & Rui, Z. (2025) · SAE technical papers on CD-ROM/SAE technical paper series · doi:10.4271/2025-01-8060
Beyond the Smudge: Simulating Opaque and Transparent Automotive Camera Lens Soiling
Eberhardt, T. D., Brühl, T., Schwager, R., Sohn, T. S., & Stork, W. (2025) · IEEE Symposia on Computational Intelligence for Energy, Transport and Environmental Sustainability (CIETES) · doi:10.1109/cietes63869.2025.10995030
“They utilized a comprehensive dataset, WoodScape [13], which includes data from four cameras with various degradation levels, categorized into ’Clear’, ’Trans-parent’, and ’Opaque’ image regions.”
InfluentialUnified interest point detection and description for perspective and Fisheye images
Xu, J., Han, D., Li, K., et al. (2025) · Scientific Reports · doi:10.1038/s41598-025-02487-w
Stereo Vision: Camera Agnostic Asymmetric Stereo
Thaler, J., Sadeghpour, E., & Pöpperl, M. (2025) · IEEE Intelligent Vehicles Symposium · doi:10.1109/iv64158.2025.11097745
“Datasets like [18], [19] provide fisheye images but not as a stereo set.”
InfluentialMask Refinement for Vehicle Camera Soiling via Linear Modeling of Border Intensity Transitions
Eberhardt, T. D., Matz, E., Schwager, R., Sohn, T. S., & Stork, W. (2025) · International Conference on Systems, Signals and Image Processing · doi:10.1109/iwssip66997.2025.11151897
“A pertinent example can be found in the creation of the WoodScape dataset [1], where 5,000 images were collected across four cameras under various soiling conditions.”
A Comparative Analysis of Multi-Modal Semantic Perception Tasks and Datasets for Mobile Robotics
Ohnemus, L., Pang, H., Zhou, L., Müller, L., & Furmans, K. (2025) · IEEE International Conference on Automation Science and Engineering (CASE) · doi:10.1109/case58245.2025.11163909
Projective Geometry and Camera Model
Li, H. (2025) · Springer eBooks · doi:10.1007/978-981-96-7344-5_4
Vision Transformer-Based Monocular Depth Estimation for Fisheye Cameras
Zhang, Z., Jiang, Y., & Jin, L. (2025) · Chinese Control Conference (CCC) · doi:10.23919/ccc64809.2025.11179747
“We have implemented the polynomial distortion model of the WoodScape dataset [9] and MEI distortion model of KITTI-360 data set[10] in the projection module.”
Feature-aligned Fisheye Object Detection Network for Autonomous Driving
Cao, H., Sun, D., Song, R., et al. (2025) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros60139.2025.11246605
“Extensive experiments conducted on the WoodScape dataset [16] demonstrate the superiority of our method over existing approaches.”
InfluentialRealistic Contaminant Image Synthesis using SPADE and Generative Label Synthesis
Byeong-Il, M., Yong-Hyeon, C., Park, J.-M., et al. (2025) · doi:10.1109/icce-asia67487.2025.11263675
“We re-classified the labels of the Woodscape dataset [5] by contaminant type, establishing a foundation for explicit control over generation.”
Rainy-nuScenes: A Data Partition for Benchmarking Contaminated Vehicle Cameras through Rain
Eberhardt, T. D., Klingler, M., Brühl, T., Schwager, R., Sohn, T. S., Schneider, S.-A., & Stork, W. (2025) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc58881.2025.11342952
“Only publicly accessible datasets are included in this analysis: As part of the WoodScape dataset [12], the authors in [2] extended the dataset by introducing various types of camera contamination, such as mud, dirt, dust and water droplets.”
The WoodScape iToF NFL LiDAR dataset
Nagiub, M., Beuth, T., Sistu, G., et al. (2025) · IEEE International Conference on Vehicular Electronics and Safety (ICVES) · doi:10.1109/icves65691.2025.11376416
“…selected a group of metrics that are widely used for depth prediction and completion tasks and valuable to detect accurately the prediction errors [15], [18]; they are as follows: Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Squared Relative Error (SqRel), and Scale-Invariant…”
Self-citationDescriptor: Occluded nuScenes Dataset (OccNuScenes)
Kumar, S., Brophy, T., Mohandas, R., et al. (2026) · IEEE data descriptions. · doi:10.1109/ieeedata.2026.3667122
“To emulate lens contamination effects in the nuScenes camera images, we apply synthetic occlusions derived from the WoodScape Soiled dataset [17].”
Self-citationIncremental Object Detection with Prompt-Based Methods
Neuwirth-Trapp, M., Bieshaar, M., Paudel, D. P., & Gool, L. V. (2025) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw69036.2025.00544
Fisheye Image Augmentation for Overcoming Domain Gaps with the Limited Dataset
Lee, H., Park, S., & Lee, M. (2025) · IEEE/CVF International Conference on Computer Vision Workshops (ICCVW) · doi:10.1109/iccvw69036.2025.00322
“The WoodScape dataset [30] presents a benchmark collection of images obtained from fisheye cameras, featuring four surround-view cameras.”
Data Augmentation is All You Need for Robust Fisheye Object Detection
Pham, L. H., Ho, Q., Vu, D. K., et al. (2025) · IEEE/CVF International Conference on Computer Vision Workshops (ICCVW) · doi:10.1109/iccvw69036.2025.00563
“Fisheye cameras are widely used in video surveillance [7, 27] and autonomous driving [31, 33] for their wide field-of-view and low cost.”
Liu, W., Wang, W., & Meng, J. (2025) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc60802.2025.11423179
“Our method was not evaluated on the WoodScape dataset [16], which features real-world driving scenes, due to the absence of groundtruth depth annotations for surround-view images.”
Beyond bounding boxes: Segmentation supervision for robust object detection in fisheye images
Oztuner, A., & Kılıçarslan, M. (2026) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2026.104798
Domain Adaptation for Urban Scene Segmentation
Saporta, A. (2022) · theses.fr (ABES) · doi:10.70675/afcffb81z0581z45d3zb31bz49fe016d8913
Segmentation of road scene omnidirectional images
Sekkat, A. R. (2022) · theses.fr (ABES) · doi:10.70675/6c5add14zaebez4287z800dzab48102c8f8b
Unlocking Constraints: Source-Free Occlusion-Aware Seamless Segmentation
Cao, Y., Zhang, J., Xu, Z., Shi, H., Peng, K., Liu, H., Yang, K., & Zhang, H. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.00838
“Wide-FoV segmentation on fisheye [13, 57, 60, 79, 81] and panoramic images [23, 36, 76, 77, 98] facilitates comprehensive 360° scene comprehension [75].”
Network-agnostic distortion-robust projections for wide-angle image understanding
Athwale, A., Ahmad, O., & Lalonde, J.-F. (2026) · IEEE Workshop/Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61042.2026.00065
“Recently, wide field-of-view (FoV) cameras have gained popularity in applications like security [37], augmented reality (AR) [56] and autonomous vehicles [18, 69].”
InfluentialBarakat, B., Islam, M. W., RASMY, M., & Hemayed, E. E. (2026) · Signal Image and Video Processing · doi:10.1007/s11760-026-05341-2
“…decouples in-domain accuracy from domain generalization: (1) Training: SynWoodscape [26], a large-scale synthetic fisheye dataset with pixel-accurate ground truth; (2) In-Domain Test: a held-out SynWoodscape split; (3) Zero-Shot Test: WoodScape [27] and Fisheye8K [28], evaluated without fine-tuning.”
Gao, W., Zhang, B., Wang, G., et al. (2026) · The Visual Computer · doi:10.1007/s00371-026-04534-7
Yang, F., Duan, X., Li, F., & Zhang, L. (2026) · Sensors · doi:10.3390/s26113533
“WoodScape [9] illustrates this limitation well.”
Occlusion-Aware Bird’s-Eye-View Semantic Segmentation for Surround-View Fisheye Cameras
Anh, N. V. T., Minh, N. H. T., Hao, N. T., et al. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-29292-6_15
Yang, T., Qian, Y., Wang, C., & Yang, M. Z. (2026) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2026.3704370
“Woodscape [23] and KITTI-360 [24] have established large-scale collections supporting various perception tasks.”
Fisheye-aware multi-camera framework for automotive near-field pedestrian localization
Barakat, B., Islam, M., RASMY, M., et al. (2026) · Neural Computing and Applications · doi:10.1007/s00521-026-12199-5
“The near-field gap : monocular height-based methods [22 ] become ill-posed below 5 m, where stature variation is indistinguishable from range change [ 15 ], yielding a baseline MAE of 1.38 m.”
InfluentialYOLO-OODNet: Attention-Guided Oriented Object Detection for Fish-Eye Imaging Sensors
Zhou, K., Liao, J., Hu, L., et al. (2026) · IEEE Sensors Journal · doi:10.1109/jsen.2026.3714195
“Woodscape [44] is a large-scale open-source fish-eye image dataset.”
Pei, J., Song, L., Liu, S., Wen, Y., Zhou, Y., Liu, Y., Liu, P., Cui, W., (24821), X. L., Ma, T., et al. (2026) · Advanced Materials · doi:10.1002/adma.74633
Liu, Y., Zhou, X., Luo, Y., et al. (2026) · Medical Image Analysis · doi:10.1016/j.media.2026.104279
Hyperbolic Maps and Objective Preserving Rewards for Learning Coverage Paths in Unknown Environments
Blake, J., & Schubert, M. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37664-0_14
Metadata
- Authors
- Yogamani, S., Hughes, C., Horgan, J., Sistu, G., Chennupati, S., Uricar, M., Milz, S., Simon, M., Amende, K., Witt, C., Rashed, H., Nayak, S., Mansoor, S., Varley, P., Perrotton, X., Odea, D., & Perez, P.
- Affiliations
- Valeo (France) (FR)
- Venue
- 2019 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2019-10
- Pages
- 9307-9317
- OpenAlex
- W2943449463
- Semantic Scholar
- 57de1a6cc6035d4ac6305121367ad3c6d3fd5104
- Cited by
- 334
- References
- 78
- Citations by year
- 2026: 32 · 2025: 55 · 2024: 61 · 2023: 74 · 2022: 47 · 2021: 40 · 2020: 16 · 2019: 9
- FWCI
- 9.05
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Keywords
- fisheye camera, surround-view cameras, autonomous driving, semantic segmentation, depth estimation, soiling detection
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