The DOI matches its Crossref record; 54 of the 72 references it cites were found in a registry or an index; 4 of the 21 sources its claims cite were checked for support.
OmniDet: Surround View Cameras Based Multi-Task Visual Perception Network for Autonomous Driving
Open accessAbstract
Surround View fisheye cameras are commonly deployed in automated driving for 360°near-field sensing around the vehicle. This work presents a multi-task visual perception network on unrectified fisheye images to enable the vehicle to sense its surrounding environment. It consists of six primary tasks necessary for an autonomous driving system: depth estimation, visual odometry, semantic segmentation, motion segmentation, object detection, and lens soiling detection. We demonstrate that the jointly trained model performs better than the respective single task versions. Our multi-task model has a shared encoder providing a significant computational advantage and has synergized decoders where tasks support each other. We propose a novel camera geometry based adaptation mechanism to encode the fisheye distortion model both at training and inference. This was crucial to enable training on the WoodScape dataset, comprised of data from different parts of the world collected by 12 different cameras mounted on three different cars with different intrinsics and viewpoints. Given that bounding boxes is not a good representation for distorted fisheye images, we also extend object detection to use a polygon with non-uniformly sampled vertices. We additionally evaluate our model on standard automotive datasets, namely KITTI and Cityscapes. We obtain the state-of-the-art results on KITTI for depth estimation and pose estimation tasks and competitive performance on the other tasks. We perform extensive ablation studies on various architecture choices and task weighting methodologies. A short video at https://youtu.be/xbSjZ5OfPes provides qualitative results.
Overview
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Written from the paper’s full text.
TL;DR
OmniDet is a multi-task visual perception network for unrectified surround-view fisheye cameras that jointly performs depth/distance estimation, visual odometry, semantic segmentation, motion segmentation, object detection, and lens soiling detection. It reports that joint training with a shared encoder and synergized decoders outperforms single-task baselines and achieves state-of-the-art KITTI depth and pose results among monocular methods.
Key findings
- The final WoodScape OmniDet (SAN10-patch) model reports RMSE 1.210, δ<1.25 0.929, semantic mIoU 78.9, motion mIoU 79.2, and detection mAP 74.1, improving over its own ablations without the proposed components.
- Multi-task learning outperformed single-task models on WoodScape, KITTI, and Cityscapes; for example, on WoodScape the multi-task setting reached distance RMSE 1.332, semantic mIoU 76.6, motion mIoU 75.3, and detection mAP 68.4, while single-task values included distance RMSE 1.681, semantic mIoU 72.5, motion mIoU 68.1, and detection mAP 63.5.
- VarNorm task weighting gave the best WoodScape task-weighting result: Sq.Rel 0.046, Abs.Rel 0.276, semantic mIoU 76.6, motion mIoU 75.3, and detection mAP 68.4, compared with equal weighting at Sq.Rel 0.058, Abs.Rel 0.302, semantic mIoU 70.3, motion mIoU 67.3, and detection mAP 64.6.
- On KITTI, OmniDet reports state-of-the-art monocular depth and pose results: depth Abs.Rel 0.092, Sq.Rel 0.657, RMSE 3.984 on the Eigen split, and pose errors of 0.009 ± 0.004 on sequence 09 and 0.008 ± 0.005 on sequence 10 with GT; with online refinement, depth improves to Abs.Rel 0.077, Sq.Rel 0.641, RMSE 3.859.
Methods
- The model uses a shared self-attention encoder with task-specific decoders for six tasks on unrectified fisheye images: distance/depth, pose/visual odometry, semantic segmentation, motion segmentation, object detection, and soiling segmentation.
- Key components include a camera geometry tensor to encode fisheye distortion and camera intrinsics, VarNorm variance-based task weighting, semantically guided distance and detection decoders via pixel-adaptive convolutions, dynamic-object masking from motion/semantic segmentation, and a 24-sided non-uniform polygon representation for fisheye object detection.
- Training and evaluation use WoodScape (10,000 images split 6:1:3, 12 cameras, three cars), KITTI, and Cityscapes; the model is trained in PyTorch for 20 epochs with batch size 24 on a Titan RTX, and inference is measured with TensorRT FP16 on NVIDIA Jetson AGX.
Limitations
- The paper states that scaling to more tasks still faces practical challenges: building a diverse and balanced dataset, corner case mining, stable training mechanisms, and designing an optimal map representation that combines all tasks while reducing post-processing.
- The paper states that the soiling dataset is independently built and cannot be trained jointly in a traditional manner; the shared encoder is frozen and only the soiling decoder is trained, and asynchronous backpropagation achieved the same accuracy.
- Evident from the text: polygon detection on raw fisheye is limited to only two classes, pedestrians and vehicles; motion labels for KITTI and Cityscapes are available only for the cars category; and additional proprietary data was used for pre-training and initialization on WoodScape.
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Claims the paper makes
23 Own interpretation · 12 Own result · 9 No full text of the sources · 6 Sources not found · 6 No basis given · 3 Couldn’t confirm · 1 Partly supported · 1 Not checked yet · accuracy not yet measured
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In the paper
“The main goal of these losses is to prevent the training objective from getting stuck at multiple local minima for homogeneous areas as fisheye images have considerably larger homogeneous areas than their rectilinear counterpart.”
§ OmniDet: Surround View Cameras based Multi-task Visual Perception Network for Autonomous Driving > A. Self-Supervised Distance and Pose Estimation Networks
What the sources say
- Partly supportedcites [11] sourceExperimental — accuracy not yet measured
The Multi-scale Estimation section says multi-scale depth prediction and image reconstruction are used to keep training from getting stuck in local minima, and discusses ambiguity in large low-texture regions. It does not attribute that effect to feature-metric losses or compare homogeneous areas in fisheye and rectilinear images. Page numbers are unavailable in our copy. (LLM, conf=0.85)
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- Rests on an assumption: Fisheye images have considerably larger homogeneous areas than their rectilinear counterpart.
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In the paper
“Quantitative results from our experiments indicate that a multitask network with 6 tasks, 5 diverse tasks perform better than the single task models along with our proposed synergies explained in Section III-B.”
§ OmniDet: Surround View Cameras based Multi-task Visual Perception Network for Autonomous Driving > B. Single Task vs Multi-Task Learning
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In the paper
“When adding the camera geometry tensor to this setting, we observe a significant increase in accuracy since we train multiple cameras with different camera intrinsics and viewing angles.”
§ OmniDet: Surround View Cameras based Multi-task Visual Perception Network for Autonomous Driving > C. Ablation Study of our Contributions
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In the paper
“This novel fusion technique in our OmniDet framework significantly improves the detection decoder's accuracy, which can be seen in Table I.”
§ OmniDet: Surround View Cameras based Multi-task Visual Perception Network for Autonomous Driving > III. NETWORK DETAILS OF MTL OMNIDET
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In the paper
“we use semantically guided features to the detection decoder as described in Section III-B2, which yields significant gains in mAP”
§ OmniDet: Surround View Cameras based Multi-task Visual Perception Network for Autonomous Driving > C. Ablation Study of our Contributions
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In the paper
“We outperform the previous methods listed in Table VI”
§ OmniDet: Surround View Cameras based Multi-task Visual Perception Network for Autonomous Driving > D. State-of-the-Art Comparison on KITTI
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In the paper
“We demonstrate the first real-time six-task model for surround-view fisheye camera perception.”
§ OmniDet: Surround View Cameras based Multi-task Visual Perception Network for Autonomous Driving > I. INTRODUCTION
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In the paper
“We also trained soiling jointly using asynchronous backpropagation [22], but it achieved the same accuracy as using the frozen encoder.”
§ OmniDet: Surround View Cameras based Multi-task Visual Perception Network for Autonomous Driving > C. Segmentation Tasks
What the sources say
- Not checked — no full text of the sourcecites [22] source
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In the paper
“We observe that uniform sampling cannot efficiently represent high curvature variations in the fisheye image object contours.”
§ OmniDet: Surround View Cameras based Multi-task Visual Perception Network for Autonomous Driving > B. Generalized Object Detection
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In the paper
“we evaluate our network on five tasks on KITTI and Cityscapes, achieving competitive results.”
§ OmniDet: Surround View Cameras based Multi-task Visual Perception Network for Autonomous Driving > V. CONCLUSION
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In the paper
“Using the online refinement method from [42], we obtain a significant improvement.”
§ OmniDet: Surround View Cameras based Multi-task Visual Perception Network for Autonomous Driving > D. State-of-the-Art Comparison on KITTI
What the sources say
- Could not verifycites [42] sourceExperimental — accuracy not yet measured
The Conclusion (p. 8) attributes improved performance to sparse convolution modules, while Boosting Single-Image Depth Prediction (pp. 14–15) discusses training with different depth maps. Neither passage addresses Casser et al.’s online refinement method or an improvement from using it; searches for “Casser” and “online refinement” found no such passage in our copy. (LLM, conf=0.76)
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In the paper
“In general, there is minimal work in the area of fisheye perception.Specifically, there is only our previous work on multi-task learning: FisheyeMultiNet [6] which discusses a more straight forward three task network.”
§ OmniDet: Surround View Cameras based Multi-task Visual Perception Network for Autonomous Driving > II. PERCEPTION TASKS AND LOSSES IN OUR MTL
What the sources say
- Could not verifycites [6] sourceExperimental — accuracy not yet measured
The Related Work section discusses single-view depth prediction and networks that jointly predict depth and segmentation, but does not address how much work exists on fisheye perception. Searches for “fisheye,” “FisheyeMultiNet,” and fisheye multi-task learning found no passage identifying FisheyeMultiNet or describing a three-task network. (LLM, conf=0.76)
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In the paper
“Shu et al. [11] proposed a convergent loss to have a relatively large convergence radius to enable gradient descent from a remote distance.”
§ OmniDet: Surround View Cameras based Multi-task Visual Perception Network for Autonomous Driving > A. Self-Supervised Distance and Pose Estimation Networks
What the sources say
- Could not verifycites [11] sourceExperimental — accuracy not yet measured
The Per-Pixel Minimum Reprojection Loss and Multi-scale Estimation sections describe Godard et al.’s depth-estimation losses, not a convergent loss proposed by Shu et al. Searches for “convergent loss,” “convergence radius,” and “Shu et al.” found no passage in our copy addressing the attributed claim. Page numbers were not available in the indexed passages. (LLM, conf=0.75)
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In the paper
“We employ the proposed VarNorm method for all the further experiments as it achieved the best results.”
§ OmniDet: Surround View Cameras based Multi-task Visual Perception Network for Autonomous Driving > D. Joint Optimization
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In the paper
“As shown in Table III, equal weighting is the worst, and our multitask network performs better than the single task networks by using any dynamic task weighting method presented above.”
§ OmniDet: Surround View Cameras based Multi-task Visual Perception Network for Autonomous Driving > D. Joint Optimization
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In the paper
“24-sided Polygon 86.6 44.6 48”
§ OmniDet: Surround View Cameras based Multi-task Visual Perception Network for Autonomous Driving > C. Ablation Study of our Contributions
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In the paper
“We demonstrate that the jointly trained model performs better than the respective single task versions.”
- Also stated in: § I. INTRODUCTION · plain
“We showcase a six-task model on WoodScape and fivetask model on KITTI and Cityscapes performing better than the single task baselines.”
§ OmniDet: Surround View Cameras based Multi-task Visual Perception Network for Autonomous Driving > Abstract
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- Also stated in: § I. INTRODUCTION · plain
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In the paper
“We obtain the state-of-the-art results on KITTI for depth estimation and pose estimation tasks and competitive performance on the other tasks.”
- Also stated in: § I. INTRODUCTION · plain
“We obtain state-of-the-art results for depth and pose estimation tasks on KITTI among monocular methods.”
§ OmniDet: Surround View Cameras based Multi-task Visual Perception Network for Autonomous Driving > Abstract
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In the paper
“Fisheye cameras have a strong radial distortion that cannot be corrected without disadvantages, including reduced field-of-view and resampling distortion artifacts at the periphery [1].”
§ OmniDet: Surround View Cameras based Multi-task Visual Perception Network for Autonomous Driving > I. INTRODUCTION
What the sources say
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In the paper
“Unlike traffic lights and traffic signs, these categories are non-rigid in nature and quite diverse in appearance, making them suitable for polygon regression.”
§ OmniDet: Surround View Cameras based Multi-task Visual Perception Network for Autonomous Driving > 1) WoodScape:
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In the paper
“All our contributed features and the synergy between tasks help the OmniDet framework to achieve a good scene understanding with high accuracy in each task's predictions.”
§ OmniDet: Surround View Cameras based Multi-task Visual Perception Network for Autonomous Driving > C. Ablation Study of our Contributions
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In the paper
“We also experiment using cylindrical rectification (Cyl Rect.), which provides a good trade-off between loss of field-of-view and reducing distortion [29].”
§ OmniDet: Surround View Cameras based Multi-task Visual Perception Network for Autonomous Driving > C. Ablation Study of our Contributions
What the sources say
- Not checked — no full text of the sourcecites [29] source
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In the paper
“Learning these points helps to regress better polygon shapes, as these points at high curvature define the shape of the object contours.”
§ OmniDet: Surround View Cameras based Multi-task Visual Perception Network for Autonomous Driving > 1) WoodScape:
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Probably not a substantial claim. The paper may give no reason why it holds. 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
“As the weights are shared in the Siamese encoder, the previous frame's encoder can be saved and re-used instead of re-computing.”
§ OmniDet: Surround View Cameras based Multi-task Visual Perception Network for Autonomous Driving > III. NETWORK DETAILS OF MTL OMNIDET
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In the paper
“Motion is a dominant cue in automotive scenes, and it requires at least two frames or the use of dense optical flow [8].”
§ OmniDet: Surround View Cameras based Multi-task Visual Perception Network for Autonomous Driving > II. PERCEPTION TASKS AND LOSSES IN OUR MTL
What the sources say
- Not checked — no full text of the sourcecites [8] source
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In the paper
“Additionally, this contribution possesses the potential to solve the infinite distance issue.”
§ OmniDet: Surround View Cameras based Multi-task Visual Perception Network for Autonomous Driving > C. Ablation Study of our Contributions
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In the paper
“Fisheye cameras are mounted low on a vehicle and are susceptible to lens soiling due to splashing of mud or water from the road.”
§ OmniDet: Surround View Cameras based Multi-task Visual Perception Network for Autonomous Driving > II. PERCEPTION TASKS AND LOSSES IN OUR MTL
What the sources say
- Not checked — no full text of the sourcecites [7] source
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In the paper
“As dynamic objects violate the static world assumption, information about their depth/distance is essential in autonomous driving; else, we would encounter the infinite depth issue during the inference stage and incur contamination to the reconstruction losses.”
§ OmniDet: Surround View Cameras based Multi-task Visual Perception Network for Autonomous Driving > III. NETWORK DETAILS OF MTL OMNIDET
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In the paper
“The proposed approach can, in principle, be applied to any fisheye projection model of choice explained in [1].”
§ OmniDet: Surround View Cameras based Multi-task Visual Perception Network for Autonomous Driving > III. NETWORK DETAILS OF MTL OMNIDET
What the sources say
- Not checked — no full text of the sourcecites [1] source
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In the paper
“Thus, generic object detection using geometric cues like motion or depth for rare objects is typically used.They will also complement the detection of standard objects and provide more robustness.”
§ OmniDet: Surround View Cameras based Multi-task Visual Perception Network for Autonomous Driving > II. PERCEPTION TASKS AND LOSSES IN OUR MTL
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- Rests on an assumption: Semantic tasks typically require a large annotated dataset covering various objects, and it is infeasible to practically cover every possible object.
In the paper
“Self-supervised methods have recently dominated depth estimation, which has also been demonstrated on fisheye images [9].”
§ OmniDet: Surround View Cameras based Multi-task Visual Perception Network for Autonomous Driving > II. PERCEPTION TASKS AND LOSSES IN OUR MTL
What the sources say
- Not matchedcites [9]
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In the paper
“We build upon this work and generalize to arbitrary camera geometries, including fisheye cameras.”
§ OmniDet: Surround View Cameras based Multi-task Visual Perception Network for Autonomous Driving > III. NETWORK DETAILS OF MTL OMNIDET
What the sources say
- Not matchedcites [30]
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In the paper
“The aforementioned training strategy makes the network camera-independent and better generalizes to images taken from a different camera.”
§ OmniDet: Surround View Cameras based Multi-task Visual Perception Network for Autonomous Driving > C. Ablation Study of our Contributions
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In the paper
“Mao et al. [4] illustrated that multi-task learning improves adversarial robustness, which is critical for safety applications.”
§ OmniDet: Surround View Cameras based Multi-task Visual Perception Network for Autonomous Driving > I. INTRODUCTION
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. 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
“These losses help the gradient descent approaches to transit smoothly towards optimal solutions.”
§ OmniDet: Surround View Cameras based Multi-task Visual Perception Network for Autonomous Driving > C. Ablation Study of our Contributions
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
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
“A single model for these four cameras instead of four individual models would also have several practical advantages such as (1) an improved efficiency on the embedded system requiring less memory and data rate to transmit, (2) an improved training by access to a larger dataset and regularization through different views, and (3) maintenance and certification of one model instead of four.”
§ OmniDet: Surround View Cameras based Multi-task Visual Perception Network for Autonomous Driving > III. NETWORK DETAILS OF MTL OMNIDET
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
In the paper
“This, in particular, breaks up the spatial invariance of the convolutions and allows the incorporation of location-specific semantic knowledge into the multi-level distance features.”
§ OmniDet: Surround View Cameras based Multi-task Visual Perception Network for Autonomous Driving > III. NETWORK DETAILS OF MTL OMNIDET
What the sources say
- Not matchedcites [32]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Not matched
In the paper
“The closest work is CAM-Convs [30], which uses camera-aware convolutions for pinhole cameras.”
§ OmniDet: Surround View Cameras based Multi-task Visual Perception Network for Autonomous Driving > III. NETWORK DETAILS OF MTL OMNIDET
What the sources say
- Not matchedcites [30]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
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
“Our multi-task model has a shared encoder providing a significant computational advantage and has synergized decoders where tasks support each other.”
§ OmniDet: Surround View Cameras based Multi-task Visual Perception Network for Autonomous Driving > Abstract
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“In the automotive multi-task setting, MultiNet [5] was one of the first to demonstrate a three task network on KITTI, and further works have primarily worked on a three task setting.”
§ OmniDet: Surround View Cameras based Multi-task Visual Perception Network for Autonomous Driving > I. INTRODUCTION
What the sources say
- Not matchedcites [5]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Not matched
None of the sources cited for this claim have been checked yet.
In the paper
“Multi-task learning (MTL) is an efficient design pattern commonly used where most of the computation is shared across all the tasks [2], [3].”
§ OmniDet: Surround View Cameras based Multi-task Visual Perception Network for Autonomous Driving > I. INTRODUCTION
What the sources say
- Not checked — no full text of the sourcecites [2] source
- Not matchedcites [3]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
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
“Variance normalization re-scales the dispersion between the different task loss distributions based on the previous n epochs.A large dispersion leads to a lower task weight, whereas a small dispersion to a higher one.Its final effect tends to homogenize the learning speed of tasks.”
§ OmniDet: Surround View Cameras based Multi-task Visual Perception Network for Autonomous Driving > D. Joint Optimization
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper relies on this without support.
Appearance variations of objects are larger due to spatially variant distortion, particularly for close-by objects.
“Appearance variations of objects are larger due to the spatially variant distortion, particularly for close-by objects.”
The paper relies on this without support.
Fisheye perception is relatively less explored despite its prevalence.
“Thus fisheye perception is a challenging task, however it is relatively less explored despite its prevalence.”
The paper relies on this without support.
Learning features for multiple tasks can act as a regularizer, improving generalization.
“Furthermore, learning features for multiple tasks can act as a regularizer, improving generalization.”
The paper relies on this without support.
Semantic tasks typically require a large annotated dataset covering various objects, and it is infeasible to practically cover every possible object.
“The semantic tasks typically require a large annotated dataset covering various objects.It is infeasible to practically cover every possible object.”
The paper relies on this without support.
The visual odometry task is required to place the detected objects in a temporally consistent map.
“Finally, the visual odometry task is required to place the detected objects in a temporally consistent map.”
The paper relies on this without support.
Fisheye images have considerably larger homogeneous areas than their rectilinear counterpart.
“as fisheye images have considerably larger homogeneous areas than their rectilinear counterpart.”
The paper relies on this without support.
Although the underlying camera intrinsics model is the same for a particular family of vehicles, manufacturing variations require calibration of each camera instance.
“Although the underlying camera intrinsics model is the same for a particular family of vehicles, there are variations due to manufacturing processes, which require the calibration of each camera instance.”
The paper relies on this without support.
Calibration can vary after deployment due to high environmental temperature or aging.
“Even after deployment, calibration can vary due to high environmental temperature or due to aging.”
The paper relies on this without support.
Public datasets have a single camera instance for both the training and test dataset.
“This contrasts with public datasets, which have a single camera instance for both the training and test dataset.”
The authors state this as an assumption.
Dynamic objects violate the static world assumption.
“As dynamic objects violate the static world assumption, information about their depth/distance is essential in autonomous driving; else, we would encounter the infinite depth issue during the inference stage and incur contamination to the reconstruction losses.”
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ OmniDet: Surround View Cameras based Multi-task Visual Perception Network for Autonomous Driving > D. Joint Optimization
“As shown in Table III, equal weighting is the worst”
§ OmniDet: Surround View Cameras based Multi-task Visual Perception Network for Autonomous Driving > C. Ablation Study of our Contributions
“Single Task 0.060 0.304 72.5 94.8 68.1 94.1 63.5 Equal 0.058 0.302 70.3 92.7 67.3 93.3 64.6 DTP [25] 0.047 0.281 75.8 95.6 75.3 95.3 67.9 DWA [26] 0.054 0.293 75.4 95.2 74.7 95.1 67.5 Geometric [27] 0.061 0.297 74.2 94.1 73.2 94.3 66.7”
Citations
Cites
Image quality assessment: from error visibility to structural similarity
Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004) · IEEE Transactions on Image Processing · doi:10.1109/tip.2003.819861 · Cited by 57,892 · cited 1×
“It is a linear combination of the general robust pixel-wise loss term [12] and the Structural Similarity (SSIM) [13] as described in [14].”
MethodFocal Loss for Dense Object Detection
Lin, T.-Y., Goyal, P., Girshick, R., He, K., & Dollar, P. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.324 · Cited by 30,581 · cited 1×
“During training, the network predicts the posterior probability Y t which is optimized in a supervised fashion by Lovasz-Softmax [20] loss, and Focal [21] loss for handling class imbalance instead of the cross-entropy loss used in our previous work.”
MethodYOLOv3: An Incremental Improvement
Redmon, J., & Farhadi, A. (2018) · arXiv · doi:10.48550/arxiv.1804.02767 · Cited by 17,141 · cited 1×
“We adapted the YOLOv3 [19] decoder to output polygons and other representations listed above for a uniform comparison.”
MethodAre 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 1×
“We systematically train and test all our single and multi-task models on Woodscape [29], a Surround View fisheye dataset with annotations for multiple tasks and on the pinhole camera datasets KITTI [35] and Cityscapes [36].”
MethodThe 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 1×
“We systematically train and test all our single and multi-task models on Woodscape [29], a Surround View fisheye dataset with annotations for multiple tasks and on the pinhole camera datasets KITTI [35] and Cityscapes [36].”
MethodVision 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 1×
“To facilitate comparison to previous methods, we also train our distance estimation method in the classical depth estimation setting on the KITTI Eigen split [39] whose results are shown in Table V.”
MethodFocal Loss for Dense Object Detection
Lin, T.-Y., Goyal, P., Girshick, R., He, K., & Dollar, P. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2858826 · Cited by 9,556
Multiscale structural similarity for image quality assessment
Wang, Z., Simoncelli, E. P., & Bovik, A. C. (2004) · The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003 · doi:10.1109/acssc.2003.1292216 · Cited by 6,253
Douglas, D. H., & Peucker, T. K. (1973) · Cartographica The International Journal for Geographic Information and Geovisualization · doi:10.3138/fm57-6770-u75u-7727 · Cited by 4,252 · cited 1×
“Then we reduce the set of points using the algorithm in [18] to get the most representative simplified curves.”
MethodMulti-task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics
Cipolla, R., Gal, Y., & Kendall, A. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00781 · Cited by 3,867 · cited 1×
“We evaluate the uncertainty loss from Kendall [23], the gradient magnitude normalization GradNorm [24], the dynamic task prioritization DTP [25], the dynamic weight average DWA [26] and the geometric loss [27].”
Method
Research works printed but not linked: 1
- geiger, 0, Proc Conf Comput Vis Pattern Recognit, 3354
Cited by
Sorted by citations
Understanding World or Predicting Future? A Comprehensive Survey of World Models
Ding, J., Zhang, Y., Shang, Y., Zhang, Y., Zong, Z., Feng, J., Yuan, Y., Su, H., Li, N., Sukiennik, N., et al. (2026) · ACM Computing Surveys · doi:10.1145/3746449 · Cited by 169
Review on Panoramic Imaging and Its Applications in Scene Understanding
Gao, S., Yang, K., Shi, H., Wang, K., & Bai, J. Y. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3216675 · Cited by 121
“other fisheye [324], [325], [326], [327], panoramic [328], [329], [330], and omnidirectional [331], [332], [333], [334]”
Surround-View Fisheye Camera Perception for Automated Driving: Overview, Survey & Challenges
Kumar, V. R., Eising, C., Witt, C., & Yogamani, S. K. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3235057 · Cited by 132
“Finally, a holistic real-time scene understanding for the near-field perception of the environment using cameras only was presented in OmniDet [123].”
MethodInfluentialSelf-citationNeuromorphic computing chip with spatiotemporal elasticity for multi-intelligent-tasking robots
Ma, S., Pei, J., Zhang, W., Wang, G., Feng, D., Yu, F., Song, C., Qu, H., Ma, C., Lü, M., et al. (2022) · Science Robotics · doi:10.1126/scirobotics.abk2948 · Cited by 70
“Moreover, the application of multiple models ( 11 – 13 ) and multi-tasking NNs ( 14 – 17 ) can further improve the ability and adaptability of robots.”
MethodSynWoodScape: Synthetic Surround-View Fisheye Camera Dataset for Autonomous Driving
Sekkat, A. R., Dupuis, Y., Kumar, V. R., Rashed, H., Yogamani, S. K., Vasseur, P., & Honeiné, P. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3188106 · Cited by 64
Self-citationZhang, J., Ma, C., Yang, K., Roitberg, A., Peng, K., & Stiefelhagen, R. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3123070 · Cited by 52
“…of panoramic data, which is often captured through distortion-pronounced fisheye lenses [38], [39], [40] or multiple surround-view cameras [41], [42], [43], is challenging as it entails a set of hard tasks like distortion elimination, camera synchronization and calibration, as well as data…”
Near-Field Perception for Low-Speed Vehicle Automation Using Surround-View Fisheye Cameras
Eising, C., Horgan, J., & Yogamani, S. K. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3127646 · Cited by 58
Self-citationEfficient Multi-Task RGB-D Scene Analysis for Indoor Environments
Seichter, D., Fischedick, S. B., Köhler, M., & Grob, H.-M. (2022) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn55064.2022.9892852 · Cited by 60
“With uncertainty weighting [52], GradNorm [53], Dynamic Weight Average [54], and VarNorm [55], several approaches for determining the weights automatically have been proposed.”
MethodVision-Based Learning for Drones: A Survey
Xiao, J., Zhang, R., Zhang, Y., & Feroskhan, M. (2025) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2025.3564184 · Cited by 65
“With dual-fisheye cameras, the drone can achieve better navigation capability [50] and omnidirectional visual perception [48], [51].”
Weather and Light Level Classification for Autonomous Driving: Dataset, Baseline and Active Learning
Dhananjaya, M. M., Kumar, V. R., & Yogamani, S. K. (2021) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc48978.2021.9564689 · Cited by 48
“enables easy integration with multi-task shared encoder models where this task can be added to existing perception model with incremental increase in complexity [22], [23].”
BackgroundSelf-citationTowards Compact Autonomous Driving Perception With Balanced Learning and Multi-Sensor Fusion
Natan, O., & Miura, J. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3149370 · Cited by 46
“We consider using the multi-task learning (MTL) approach since handling each task with a single-task model can be very costly and inefficient [12] [13] [14].”
Ma, C., Zhang, J., Yang, K., et al. (2021) · doi:10.1109/itsc48978.2021.9564920 · Cited by 38
“Segmentation of panoramic data, which is often captured through distortion-heavy fisheye lenses [22], [23] or multiple cameras [2], [24], is especially challenging as it requires eliminating distortions, synchronizing and calibrating the cameras, and fusing the data, which leads to higher latency.”
Multisensor Information Fusion: Future of Environmental Perception in Intelligent Vehicles
Zhang, Y., Tu, C., Gao, K., & Wang, L. (2024) · Journal of Intelligent and Connected Vehicles · doi:10.26599/jicv.2023.9210049 · Cited by 48
Survey on Multi-Task Learning in Smart Transportation
Alzahrani, M. Y., Wang, Q., Liao, W., Chen, X., & Yu, W. (2024) · IEEE Access · doi:10.1109/access.2024.3355034 · Cited by 35
“…motion segmentation to differentiate between car movement and other objects movement, object detection, and lens soiling detection to detect when the camera vision is not clear, for example, by dirt [76], synergized decoders was used in this method to make the tasks related to each other.”
MethodLiMoSeg: Real-time Bird’s Eye View based LiDAR Motion Segmentation
Mohapatra, S., Hodaei, M., Yogamani, S. K., Milz, S., Gotzig, H., Simón, M., Rashed, H., & Maeder, P. (2022) · International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) · doi:10.5220/0010866000003124 · Cited by 31
Self-citationDeep Learning for Omnidirectional Vision: A Survey and New PerspectivesPreprint
Ai, H., Cao, Z., Zhu, J., Bai, H., Chen, Y., & Wang, L. (2022) · doi:10.36227/techrxiv.19807699 · Cited by 36
LF-VIO: A Visual-Inertial-Odometry Framework for Large Field-of-View Cameras with Negative Plane
Wang, Z., Yang, K., Shi, H.-W., Li, P., Gao, F., & Wang, K.-W. (2022) · IEEE/RJS International Conference on Intelligent RObots and Systems · doi:10.1109/iros47612.2022.9981217 · Cited by 24
“Moreover, based on different sensor types, many other odometry estimation methods emerge in the field, including fisheye-based [29]– [33], multi-camera-based [3], [34]–[38], and systems that combine omnidirectional imaging with other sensing units like GPS [39] and LiDAR sensors [40], [41].”
BackgroundJaus, A., Yang, K., & Stiefelhagen, R. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3232897 · Cited by 20
“(FoV), many surrounding understanding platforms are based on fisheye images or multiple cameras [63], [64], [65], [66],”
BackgroundReview on Functional Testing Scenario Library Generation for Connected and Automated Vehicles
Zhu, Y., Wang, J., Meng, F., & Liu, T. (2022) · Sensors · doi:10.3390/s22207735 · Cited by 19
Ai, H., & Wang, L. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.00947 · Cited by 23
“…environ ment in one shot with an ultra-wide field-of-view (FoV) of 180°x360°, which is broadly applied to applications, e.g., autonomous driving [1, 19, 42, 43, 49] and virtual reality *Corresponding author (e-mail: linwang@ust.hk) With only lM more parameters, our depth result is more accurate.”
BackgroundZhou, J. N., Yang, D., Song, T., et al. (2024) · Image and Vision Computing · doi:10.1016/j.imavis.2024.104966 · Cited by 18
A Review of Vision-Based Multi-Task Perception Research Methods for Autonomous Vehicles
Wang, H., Li, J., & Dong, H. (2025) · Sensors · doi:10.3390/s25082611 · Cited by 20
“OmniDet [88] is a multi-task visual perception system for surround-view fisheye lenses.”
An Online Learning System for Wireless Charging Alignment Using Surround-View Fisheye Cameras
Dahal, A., Kumar, V. R., Yogamani, S. K., & Eising, C. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3182165 · Cited by 20
“While we provide a short overview of the baseline model here, the reader is referred to [31] for more complete details.”
BackgroundMethodInfluentialSelf-citationAdversarial Attacks on Multi-task Visual Perception for Autonomous Driving
Sobh, I., Hamed, A., Kumar, V. R., & Yogamani, S. K. (2021) · Journal of Imaging Science and Technology · doi:10.2352/j.imagingsci.technol.2021.65.6.060408 · Cited by 18
Self-citationWu, Z., Gan, Y., Li, X., Wu, Y., Wang, X., Xu, T., & Wang, F. (2022) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2022.3218594 · Cited by 16
“areas, valet parking aims to drive the vehicle into the drop-off area such as parking slots, which faces high requirements in perception [12].”
BackgroundMethodFishDreamer: Towards Fisheye Semantic Completion via Unified Image Outpainting and Segmentation
Shi, H., Li, Y., Yang, K., Zhang, J., Peng, K., Roitberg, A., Ye, Y., Ni, H., Wang, K., & Stiefelhagen, R. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00684 · Cited by 18
A Hybrid Sparse-Dense Monocular SLAM System for Autonomous Driving
Gallagher, L., Kumar, V. R., Yogamani, S. K., & McDonald, J. (2021) · European Conference on Mobile Robots · doi:10.1109/ecmr50962.2021.9568797 · Cited by 17
Self-citationMumuni, F., Mumuni, A., & Amuzuvi, C. K. (2022) · Machine Learning with Applications · doi:10.1016/j.mlwa.2022.100416 · Cited by 20
PGDS-YOLOv8s: An Improved YOLOv8s Model for Object Detection in Fisheye Images
Yang, D., Zhou, J. N., Song, T., Zhang, X., & Song, Y. (2023) · Applied Sciences · doi:10.3390/app14010044 · Cited by 14
SlaBins: Fisheye Depth Estimation using Slanted Bins on Road Environments
Lee, J.-S., Cho, G., Park, J., Kim, K., Lee, S., Kim, J.-H., Jeong, S.-G., & Joo, K. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00805 · Cited by 16
“Overall, SlaBins outperforms the other baseline approaches [3, 20, 21].”
BackgroundMethodResultInfluentialOptical flow for autonomous driving: Applications, challenges and improvements
Shen, S., Kerofsky, L. J., & Yogamani, S. K. (2023) · Electronic Imaging · doi:10.2352/ei.2023.35.16.avm-128 · Cited by 16
Self-citationDeep Learning for Omnidirectional Vision: A Survey and New PerspectivesPreprint
Ai, H., Cao, Z., Zhu, J., Bai, H., Chen, Y., & Wang, L. (2022) · doi:10.36227/techrxiv.19807699.v1 · Cited by 15
Cross-task and time-aware adversarial attack framework for perception of autonomous driving
Lu, Y., Liu, N., Li, Y., Chen, J., & Velipasalar, S. (2025) · Pattern Recognition · doi:10.1016/j.patcog.2025.111652 · Cited by 14
Djenouri, Y., Srivastava, G., Djenouri, D., et al. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3165156 · Cited by 14
“[27] developed a deep learning network to identify vehicles captured in fisheye images.”
BackgroundBEV-MODNet: Monocular Camera based Bird's Eye View Moving Object Detection for Autonomous Driving
Rashed, H., Essam, M., Mohamed, M., Sallab, A. E., & Yogamani, S. K. (2021) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc48978.2021.9564667 · Cited by 15
“On the other hand, the model in [10] takes two sequential images as input and tries to understand motion implicitly.”
BackgroundMethodSelf-citationInfluence of Camera-LiDAR Configuration on 3D Object Detection for Autonomous Driving
Li, Y., Hu, H., Liu, Z., Xu, X., Huang, X., & Zhao, D. X. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10610896 · Cited by 18
“With this respect, Li-DARs and cameras are widely used for the perception of autonomous vehicles due to their capability to obtain rich information from the environment in real-time settings [21]– [23].”
BackgroundSong, F., Ma, R., Leí, T., & Peng, Z. (2023) · Remote Sensing · doi:10.3390/rs15092364 · Cited by 13
Hernandez‐Diaz, K., Alonso‐Fernandez, F., & Bigün, J. (2023) · IEEE Access · doi:10.1109/access.2023.3315234 · Cited by 12
“…editor coordinating the review of this manuscript and approving it for publication was Genoveffa Tortora . and extract highly discriminative features, as well as to easily adapt to different applications such as medical data [6], autonomous driving [7], or, in our case, biometric recognition [8].”
BackgroundAdvanced Techniques for Perception and Localization in Autonomous Driving Systems: A Survey
Sellat, Q., & Ramasubramanian, K. (2022) · Optical Memory and Neural Networks · doi:10.3103/s1060992x22020084 · Cited by 12
DarSwin: Distortion Aware Radial Swin Transformer
Athwale, A., Afrasiyabi, A., Lagüe, J., et al. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00545 · Cited by 9
Adaptive Attention Module for Image Recognition Systems in Autonomous Driving
Ma, X., Hu, K., Sun, X., & Chen, S. (2024) · International Journal of Intelligent Systems · doi:10.1155/2024/3934270 · Cited by 9
Video Waterdrop Removal via Spatio-Temporal Fusion in Driving Scenes
Wen, Q., Wu, Y., & Chen, Q. (2023) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48891.2023.10161240 · Cited by 13
“With the increasing applications of visual perception methods [1], [2], [3], [4] in robotics and autonomous driving, the robustness of these methods has become more and more important in their visual system.”
MethodSemantics and Contour Based Interactive Learning Network for Building Footprint Extraction
Zhu, X., Zhang, X., Zhang, T., Tang, X., Chen, P., Zhou, H., & Jiao, L. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3317080 · Cited by 9
“OmniDet [39] is aimed at a multitasking visual perception system for circumferential fisheye lenses, integrating multiple tasks of depth estimation, visual odometry, semantic and motion segmentation, object detection, and lens contamination detection.”
BackgroundFisheyeSuperPoint: Keypoint Detection and Description Network for Fisheye Images
Konrad, A., Eising, C., Sistu, G., McDonald, J., Villing, R. C., & Yogamani, S. K. (2022) · International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) · doi:10.5220/0010795400003124 · Cited by 10
“Such multi-task models share the same encoder, which can improve performance and reduce latency for all tasks (Leang et al., 2020), (Ravikumar et al., 2021).”
BackgroundMethodSelf-citationLearning Panoptic Segmentation from Instance Contours
Chennupati, S., Narayanan, V., Sistu, G., Yogamani, S. K., & Rawashdeh, S. A. (2021) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48506.2021.9560798 · Cited by 9
Self-citationFisheyePixPro: Self-supervised pretraining using Fisheye images for semantic segmentation
Cheke, R., Sistu, G., Eising, C., van de Ven, P., Kumar, V. R., & Yogamani, S. (2022) · Autonomous Vehicles and Machines · doi:10.2352/ei.2022.34.16.avm-147 · Cited by 11
“Several experiments have been conducted to enhance the performance of CNN on fisheye dataset by investigating the impact of adversarial attacks[15] on a multi-task visual perception network [16].”
BackgroundSelf-citationCityscapes TL++: Semantic Traffic Light Annotations for the Cityscapes Dataset
Janosovits, J. (2022) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra46639.2022.9812144 · Cited by 9
Neuroevolutionary multi-objective approaches to trajectory prediction in autonomous vehicles
Stapleton, F., Galván, E., Sistu, G., & Yogamani, S. K. (2022) · Genetic and Evolutionary Computation Conference Companion (GECCO Companion) · doi:10.1145/3520304.3528984 · Cited by 9
Self-citationSwin‐fisheye: Object detection for fisheye images
Zhang, D., Yang, T., & Zhao, B. (2024) · IET Image Processing · doi:10.1049/ipr2.13216 · Cited by 8
Automotive DNN-Based Object Detection in the Presence of Lens Obstruction and Video Compression
Baris, G., Li, B., Chan, P. H., Avizzano, C. A., & Donzella, V. (2025) · IEEE Access · doi:10.1109/access.2025.3544773 · Cited by 7
A Review On Thermal Infrared Semantic Distribution for Nightfall Drive
Bandi, M., & Reeja, S. R. (2022) · International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT) · doi:10.1109/icicict54557.2022.9917651 · Cited by 8
“WoodScape[31] is the very first dataset with a fisheye lens acquired by four a vehicle with fisheye cameras for autonomous driving.”
BackgroundInfluentialLu, R., Shang, J., Wu, J. Y., et al. (2025) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2024.3523287 · Cited by 7
“Thecontinuous development of optical imaging technology and computer vision has enabled the wide usage of panoramic vision in fields such as robotics [4], [5] and autonomous driving [6], [7].”
Internal thread defect detection system based on multi-vision
Dou, X., Xue, C., Zhang, G., & Jiang, Z. (2024) · PLoS ONE · doi:10.1371/journal.pone.0304224 · Cited by 9
Ensemble-Based Semi-Supervised Learning to Improve Noisy Soiling Annotations in Autonomous Driving
Uřičář, M., Sistu, G., Yahiaoui, L., & Yogamani, S. K. (2021) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc48978.2021.9565086 · Cited by 6
“In general, there is limited work on fisheye camera perception for tasks such as object detection [1], depth estimation [2], SLAM [3] and multi-task learning [4].”
BackgroundSelf-citationTrained Trajectory based Automated Parking System using Visual SLAM on Surround View CamerasPreprint
Tripathi, N., & Yogamani, S. K. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2001.02161 · Cited by 7
Self-citationLiDAR-BEVMTN: Real-Time LiDAR Bird’s-Eye View Multi-Task Perception Network for Autonomous Driving
Mohapatra, S., Yogamani, S. K., Kumar, V. R., Milz, S., Gotzig, H., & Mäder, P. (2025) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2024.3510642 · Cited by 5
“It builds on our previous work OmniDet [42] is a multi-task camera perception model.”
MethodSelf-citationWu, Z., Zhang, W., Wang, J., Wang, M., Gan, Y., Gou, X., Fang, M., & Song, J. (2021) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros51168.2021.9636707 · Cited by 5
“Moreover, OmniDet [33] presents a multi-task visual perception network to capture the surrounding environment.”
BackgroundCyber-Attacks on Wheeled Mobile Robotic Systems with Visual Servoing Control
Jokić, A., Khazraei, A., Petrović, M., et al. (2023) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros55552.2023.10341376 · Cited by 5
“Robotic systems rely heavily on visual information for a wide variety of tasks such as control [21], perception [22], decision-making, and planning [23].”
BackgroundSTRIDE: Street View-based Environmental Feature Detection and Pedestrian Collision Prediction
González, C., Ayobi, N., Escallón, F., Baldovino-Chiquillo, L., Wilches-Mogollon, M. A., Pasos, D., Ramírez, N., Pinzón, J. D., Sarmiento, O. L., Quistberg, D. A., et al. (2023) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw60793.2023.00347 · Cited by 5
“Further approaches use data gathered with fisheye and surround-view cameras annotated for semantic scene parsing [24, 92, 29, 43, 49], thus introducing significant distortion and deformation caused by this type of cameras.”
MethodLearning Cross-modality Interaction for Robust Depth Perception of Autonomous Driving
Liang, Y., Chen, N., Yu, Z., Tang, L., Yu, H., Guo, B., & Zeng, D. D. (2024) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3650039 · Cited by 5
“For example, although depth sensing based on RGB images can provide high-resolution depth information, its performance is limited by lower accuracy and short perception distance [14, 19, 33, 52, 57].”
BackgroundCao, H., Li, Y., Liu, Y., Li, X., Chen, G., & Knoll, A. (2024) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros58592.2024.10802087 · Cited by 5
“The training process remains consistent with the source code released by OmniDet [18].”
InfluentialMa, C., Zhang, J., Yang, K., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2108.06383 · Cited by 5
“Segmentation of panoramic data, which is often captured through distortion-heavy fisheye lenses [22], [23] or multiple cameras [2], [24], is especially challenging as it requires eliminating distortions, synchronizing and calibrating the cameras, and fusing the data, which leads to higher latency.”
VM-MODNet: Vehicle Motion aware Moving Object Detection for Autonomous Driving
Rashed, H., Sallab, A. E., & Yogamani, S. K. (2021) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc48978.2021.9564662 · Cited by 4
“We start with the OmniDet [18] motion segmentation network using two-stream RGB only network.”
MethodSelf-citationA Comparison of Spherical Neural Networks for Surround-View Fisheye Image Semantic Segmentation
Manzoor, A., Mohandas, R., Scanlan, A., Grua, E. M., Collins, F., Sistu, G., & Eising, C. (2025) · IEEE Open Journal of Vehicular Technology · doi:10.1109/ojvt.2025.3541891 · Cited by 5
“A common yet simplistic approach for applying CNNs to fisheye imagery involves rectifying fish-eye images before applying standard algorithms; however, this method introduces significant drawbacks, such as a reduced field of view and resampling distortion artifacts near the edges [19].”
SpikiLi: A Spiking Simulation of LiDAR based Real-time Object Detection for Autonomous Driving
Mohapatra, S., Mesquida, T., Hodaei, M., Yogamani, S. K., Gotzig, H., & Maeder, P. (2022) · International Conference on Event-based Control, Communication, and Signal Processing · doi:10.1109/ebccsp56922.2022.9845647 · Cited by 5
Self-citationDarSwin-Unet: Distortion Aware Architecture
Athwale, A., Shili, I., Bergeron, É., et al. (2025) · IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv61041.2025.00840 · Cited by 3
“Similar to our method, [20,21,23,25] propose a camera-aware depth estimation network to handle the severe distortion of fisheye cameras: [25] encode the camera intrinsic parameters as a tensor; and [23] propose a self-supervised depth estimation method which relies on the lens distortion parameter…”
A Feature-based Approach for the Recognition of Image Quality Degradation in Automotive Applications
Bauer, F. (2023) · International Conference on Systems, Signals and Image Processing · doi:10.1109/iwssip58668.2023.10180245 · Cited by 3
“They allow the system to issue warnings about malfunction or even to disable affected functions [2]–[4].”
BackgroundFishSegSSL: A Semi-Supervised Semantic Segmentation Framework for Fish-Eye Images
Paul, S., Patterson, Z., & Bouguila, N. (2024) · Journal of Imaging · doi:10.3390/jimaging10030071 · Cited by 3
“Another key observation we find is that the very recent literature is based on multimodal or multitask models where the signals from different tasks or domains help the model to learn representation from fish-eye images [4,27].”
BackgroundMethodA comprehensive study of fisheye image compression and perception for autonomous driving
Barakat, B., Sobh, I., Wong, C.-C., et al. (2025) · Neural Computing and Applications · doi:10.1007/s00521-024-10831-w · Cited by 3
Fisheye object detection based on standard image datasets with 24-points regression strategy
Xu, X., Gao, Y., Liang, H. Z., Yang, Y. M., & Fu, M. (2022) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros47612.2022.9981891 · Cited by 3
“Rashed et. al [1], [14] adjusted the encoder part of the standard image object detection network.”
BackgroundCooperative Environmental Perception Task Offloading for Connected and Autonomous Vehicles
Lv, P., Huang, J., & Liu, H. (2023) · Electronics · doi:10.3390/electronics12173714 · Cited by 3
Autonomous Driving of Personal Mobility by Imitation Learning from Small and Noisy Dataset
Kobayashi, T., & Enomoto, T. (2024) · IEEE/SICE International Symposium on System Integration (SII) · doi:10.1109/sii58957.2024.10417607 · Cited by 3
“Note that although comparisons with the state-of-the-art methods (e.g. [23], [24]) are desirable in terms of autonomous driving technology, they are omitted in this paper because it is difficult to match the implementation conditions, making fair comparisons difficult.”
ZSPose: Instance-Level Zero-Shot Object Pose Estimation With Segment Anything Model
Yu, S. W., Zhai, D., Yin, J., et al. (2025) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2025.3618671 · Cited by 2
“I NTRODUCTION 6 D POSE estimation is an important task in the field of computer vision, widely applied in areas such as robotic grasping [1], [2], [3], autonomous driving [4], and augmented reality [5].”
BNDCNet: Bilateral nonlocal decoupled convergence network for semantic segmentation
Ye, M., Chen, Z., Guo, Y., et al. (2023) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2023.104028 · Cited by 2
Robust Detection for Fisheye Camera Based on Contrastive Learning
Zhang, J., Tang, L., & Zhou, X. (2025) · Comput. Mater. Continua · doi:10.32604/cmc.2025.061690 · Cited by 3
“Fisheye cameras offer an omnidirectional field of view and can capture comprehensive visual information, making them particularly advantageous for applications in intelligent surveillance [1–3], unmanned aerial vehicles [4,5], virtual reality [6,7], autonomous driving [8–10], and robotics [11–13].”
Design of a preprocessing system suitable for Surround View Monitor based on Zynq
Oh, H., Hwang, G. H., Sim, H., et al. (2025) · International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC) · doi:10.1109/itc-cscc66376.2025.11137632 · Cited by 2
“Recently, SVMs have become a core component of advanced driver assistance systems (ADAS), and SVM technology has been actively researched in autonomous driving beyond parking [1].”
Zhang, Z., Xu, R., Zhang, W., Zhang, Z., Tan, X., Gong, J., Xie, Y., & Ma, L. (2025) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3746027.3755159 · Cited by 2
“Mean-while, current multi-view depth estimation methods primarily target homogeneous setups, either pinhole-only [15, 27] or fisheye-only [16, 30], leaving the great potential of heterogeneous pinhole-fisheye depth estimation unexplored.”
Advancements in Fisheye Lens Image Stitching for Enhanced ADAS in Automated Vehicles
Harsha Vardhan Reddy., K., & Hegde, V. (2023) · International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS) · doi:10.1109/csitss60515.2023.10334173 · Cited by 2
“In order to embed the fisheye distortion model in both training and inference, they proposed a new adaption approach[10] based on camera geometry.”
MethodSemantic 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
“While some prior works have focused on semantic segmentation of fisheye images, they either rely on multi-task or multi-modal approaches, leveraging learning signals from multiple tasks or modalities [5], [13].”
BackgroundDialesiotis, S., Papatsimouli, M., Asimopoulos, K. N., & Fragulis, G. F. (2024) · AIP conference proceedings · doi:10.1063/5.0234982 · Cited by 1
Sequential Cooperative Distillation for Imbalanced Multi-Task Learning
Feng, Q., Yao, J.-Y., Xie, M.-K., et al. (2024) · Journal of Computer Science and Technology · doi:10.1007/s11390-024-2264-z · Cited by 1
Fisheye omnidirectional stereo depth estimation assisted with edge-awareness
Sun, J., Xue, H., Guo, S., et al. (2025) · Frontiers in Physics · doi:10.3389/fphy.2025.1555785 · Cited by 1
Omnidirectional Spatial Modeling from Correlated Panoramas
Zhang, X., Fu, T., & Xu, Z. (2025) · ACM International Conference on Multimedia in Asia (MMAsia) · doi:10.1145/3743093.3770977 · Cited by 1
“Omnidirectional scene understanding with 360 ◦ cameras plays important roles in a wide range of applications, such as embodied AI, autonomous driving, and virtual/augmented reality (VR/AR) [7, 13, 16].”
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
“To alleviate the above distortion problem in fisheye images, self-supervised fisheye monocular depth estimation methods for fisheye cameras [2], [10], [11], [12], [13], [14] have been explored in recent years.”
InfluentialMulti-lane modelling using convolutional neural networks and conditional random fields
Babu, G., Cheke, R., Sistu, G., & Yogamani, S. K. (2022) · Electronic Imaging · doi:10.2352/ei.2022.34.16.avm-148 · Cited by 1
Self-citationRobust Single-Cam Surround View Object Detection and Localization Using Memory Maps
Quan, Y., Kiefer, B., Messmer, M., Akupati, C. R., Graser, R., & Zell, A. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-78113-1_8 · Cited by 1
Mumuni, F., Mumuni, A., & Amuzuvi, C. K. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4122364 · Cited by 1
Ramachandran, S., Sistu, G., McDonald, J., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.08246
Self-citationPerformance monitoring of deep learning vision systems during deployment
Rahman, Q. M. (2022) · Queensland University of Technology · doi:10.5204/thesis.eprints.229733
Relaxing the Forget Constraints in Open World Recognition
Fontanel, D., Cermelli, F., Geraci, A., et al. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-06427-2_62
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
Bndcnet: Bilateral Nonlocal Decoupled Convergence Network for Semantic SegmentationPreprint
Ye, M., Chen, Z., Guo, Y., et al. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4505116
Impact of Misperception on Emergence of Risk in Platoon of Autonomous Vehicles
Amini, A., Liu, G., Pandey, V. K., & Motee, N. (2023) · IEEE Conference on Decision and Control (CDC) · doi:10.1109/cdc49753.2023.10383863
“On the other hand, with recent advancements in visual perception algorithms[9], almost all cars are equipped with cameras that allow them to perceive the environments for safety measures and collision avoidance.”
BackgroundJoint-Loss Enhanced Self-Supervised Learning for Refinement-Coupled Object 6D Pose Estimation
Mu, F., Sun, S., Huang, R., et al. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10611061
“6D object pose estimation holds great potential in various domains such as robotic grasping [1][2], autonomous driving [3][4], and augmented reality [5][6].”
BackgroundSynthetic 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
“Many existing researches attempt to perform soil detection or segmentation [11], [22].”
DepthFisheye: Efficient Fine-Tuning of Depth Estimation Models for Fisheye Cameras
Wu, W., Zhang, Z., Tan, X., Zhang, Z., & Ma, L. (2025) · Lecture notes in computer science · doi:10.1007/978-981-96-5815-2_1
Research on a Vision-Based Perception Strategy for Autonomous Vehicle Control
Xie, S. (2024) · International Conference on Information Technologies and Electrical Engineering (ICITEE) · doi:10.1145/3717934.3717994
UST-SU: a U-shaped video prediction network based on partial autoregression
Cui, Z., Tian, W., Luo, F., Liu, Q., & Jiang, S. (2025) · The Journal of Supercomputing · doi:10.1007/s11227-025-07486-2
“The modeling capability of motion patterns within videos has made it applicable in diverse tasks such as traffic flow prediction [1, 2], precipitation nowcasting [3, 4], human action recognition [5–7] and autonomous driving [8, 9].”
Unified 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
GF-Net: Ghosting-fusion network for autonomous driving
Liu, C., Zhang, Z., Yao, J., et al. (2025) · Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering · doi:10.1177/09544070251350929
Metadata
- Authors
- Ravi Kumar, V., Yogamani, S., Rashed, H., Sitsu, G., Witt, C., Leang, I., Milz, S., & Mader, P.
- Affiliations
- Valeo (Ireland) (IE)
- Valeo (Germany) (DE)
- Valeo (France) (FR)
- Technische Universität Ilmenau (DE)
- Venue
- IEEE Robotics and Automation Letters
- Published
- 2021-04
- Volume
- 6
- Issue
- 2
- Pages
- 2830-2837
- ISSN
- 2377-3766 (ISSN-L)
- 2377-3774
- OpenAlex
- W3129414859
- Semantic Scholar
- 58fce566cc744fac4851353ee3e6132e8fbe20e5
- Cited by
- 121
- References
- 72
- Citations by year
- 2026: 15 · 2025: 25 · 2024: 19 · 2023: 23 · 2022: 26 · 2021: 12
- FWCI
- 7.05
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Robotics and Automation Letters (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- 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
- Keywords
- surround-view cameras, fisheye images, multi-task learning, autonomous driving, depth estimation, visual odometry, semantic segmentation, motion segmentation, object detection, shared encoder, KITTI, cityscape
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