The DOI matches its Crossref record; 57 of the 59 references it cites were found in a registry or an index; none of the 18 sources its claims cite is checked for support yet.
3D Packing for Self-Supervised Monocular Depth Estimation
Open accessAbstract
Although cameras are ubiquitous, robotic platforms typically rely on active sensors like LiDAR for direct 3D perception. In this work, we propose a novel self-supervised monocular depth estimation method combining geometry with a new deep network, PackNet, learned only from unlabeled monocular videos. Our architecture leverages novel symmetrical packing and unpacking blocks to jointly learn to compress and decompress detail-preserving representations using 3D convolutions. Although self-supervised, our method outperforms other self, semi, and fully supervised methods on the KITTI benchmark. The 3D inductive bias in PackNet enables it to scale with input resolution and number of parameters without overfitting, generalizing better on out-of-domain data such as the NuScenes dataset. Furthermore, it does not require large-scale supervised pretraining on ImageNet and can run in real-time. Finally, we release DDAD (Dense Depth for Automated Driving), a new urban driving dataset with more challenging and accurate depth evaluation, thanks to longer-range and denser ground-truth depth generated from high-density LiDARs mounted on a fleet of self-driving cars operating world-wide.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
PackNet is a self-supervised monocular depth architecture using symmetrical 3D packing/unpacking blocks, trained on unlabeled monocular videos, optionally with velocity supervision for metric scale. It reports state-of-the-art KITTI results, better generalization on NuScenes, real-time inference, and releases the DDAD dataset for long-range dense depth evaluation.
Key findings
- On DDAD, PackNet-SfM achieves Abs Rel 0.162, Sq Rel 3.917, RMSE 13.452, RMSE log 0.269, and δ<1.25 0.823, outperforming Monodepth2 (R18) at Abs Rel 0.381 and RMSE 21.277; the gap increases at longer ranges up to 200m.
- On NuScenes generalization without fine-tuning, PackNet reaches Abs Rel 0.187, Sq Rel 1.852, RMSE 7.636, RMSE log 0.289, and δ<1.25 0.742, better than ResNet50 (Abs Rel 0.216, RMSE 8.477) and ResNet50‡ (Abs Rel 0.210, RMSE 8.111).
- Ablation on KITTI at 640×192 shows PackNet with D=8 3D filters reaches Abs Rel 0.111, Sq Rel 0.785, RMSE 4.601, RMSE log 0.189, and δ<1.25 0.878, improving over no pack/unpack (Abs Rel 0.121, RMSE 4.831).
- PackNet reaches about 128M parameters with 60ms inference on a Titan V100, improvable to <30ms with TensorRT; a packing/unpacking reconstruction test achieves loss 0.0079 versus 0.063 for max-pooling/bilinear upsampling.
Methods
- Self-supervised monocular SfM jointly trains a depth network and pose network on unlabeled monocular videos using a photometric loss (SSIM + L1), minimum over source images, auto-masking, and edge-aware depth smoothness; an optional velocity supervision loss aligns pose-translation magnitude with measured velocity to resolve scale ambiguity.
- PackNet is a symmetrical encoder-decoder with packing blocks (Space2Depth, 3D convolution expansion, 2D contraction) and unpacking blocks (2D convolution, 3D convolution, Depth2Space), plus skip connections and multiple inverse-depth outputs.
- Training uses KITTI (39,810 train, 4,424 validation, 697 evaluation; improved ground truth 652 maps), DDAD (17,050 train and 4,150 evaluation frames, Luminar-H2, up to 200m), NuScenes (6,019 front-facing validation images), and CityScapes (88,250 training images) for pretraining; PyTorch on 8 Titan V100 GPUs, Adam, 100 epochs, batch size 4, depth LR 2e-4, pose LR 5e-4, α=0.85, λ1=0.001, λ2=0.05.
Limitations
- The paper states monocular SfM is inherently scale-ambiguous; metric scale requires velocity supervision at training time, otherwise test-time LiDAR ground-truth scaling is needed.
- The paper states DDAD experiments use only the front-facing camera, and that packing/unpacking do not ensure invertibility.
- No explicit limitations section is provided; evident constraints include KITTI evaluation up to 80m and DDAD up to 200m, and pose results are deferred to supplementary material due to space.
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27 Own result · 14 Not checked yet · 13 No basis given · 12 Own interpretation
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In the paper
“iii) generalizes better on unseen data;”
§ 3D Packing for Self-Supervised Monocular Depth Estimation > 1. Introduction
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In the paper
“ii) is competitive with fully supervised methods;”
§ 3D Packing for Self-Supervised Monocular Depth Estimation > 1. Introduction
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“While recent works in self-supervised monocular depth estimation have mostly focused on engineering the loss function [6,34,48,54], we show that performance critically depends on the model architecture, in line with the observations of [28] for other self-supervised tasks.”
§ 3D Packing for Self-Supervised Monocular Depth Estimation > 1. Introduction
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- Not checked yetcites [28] source
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“v) can run in real time at high resolution;”
§ 3D Packing for Self-Supervised Monocular Depth Estimation > 1. Introduction
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“Our experiments on the standard KITTI benchmark [17], the recent NuScenes dataset [5], and our new proposed DDAD benchmark show that our self-supervised monocular approach i) improves on the state of the art, especially at longer ranges;”
§ 3D Packing for Self-Supervised Monocular Depth Estimation > 1. Introduction
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In the paper
“vi) does not require supervised pretraining on ImageNet to achieve state-of-the-art results;”
§ 3D Packing for Self-Supervised Monocular Depth Estimation > 1. Introduction
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In the paper
“or test-time ground-truth scaling if velocity information is available at training time.”
§ 3D Packing for Self-Supervised Monocular Depth Estimation > 1. Introduction
plaindirect- Asserted: passes — The authors put it forward as true.
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- Rests on an assumption: Instantaneous velocity measurements are ubiquitous in most mobile systems today.
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
“Finally, we release DDAD (Dense Depth for Automated Driving), a new urban driving dataset with more challenging and accurate depth evaluation, thanks to longer-range and denser ground-truth depth generated from high-density LiDARs mounted on a fleet of self-driving cars operating world-wide.”
§ 3D Packing for Self-Supervised Monocular Depth Estimation > Abstract
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In the paper
“especially as more 3D convolutional filters are added”
§ 3D Packing for Self-Supervised Monocular Depth Estimation > 5.6. Ablation Studies
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“Furthermore, we show that by simply introducing an additional source of unlabeled videos, such as the publicly available CityScapes dataset (CS+K) [9], we are able to further improve monocular depth estimation performance.”
§ 3D Packing for Self-Supervised Monocular Depth Estimation > 5.3. Depth Estimation Performance
What the sources say
- Not checked yetcites [9] source
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In the paper
“iv) scales better with number of parameters, input resolution, and more unlabeled training data;”
§ 3D Packing for Self-Supervised Monocular Depth Estimation > 1. Introduction
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In the paper
“Additionally, by leveraging during training only weak velocity information, we are able to make our model scale-aware, i.e. producing metrically accurate depth maps from a single image.”
§ 3D Packing for Self-Supervised Monocular Depth Estimation > 6. Conclusion
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In the paper
“Although purely trained on unlabeled monocular videos, our approach outperforms other existing selfand semi-supervised methods and is even competitive with fully-supervised methods while able to run in real-time.”
§ 3D Packing for Self-Supervised Monocular Depth Estimation > 6. Conclusion
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“As indicated by Pillai et al. [39], we also observe an improvement in performance at higher image resolutions, which we attribute to the proposed network's ability to properly preserve and process spatial information end-to-end.”
§ 3D Packing for Self-Supervised Monocular Depth Estimation > 5.3. Depth Estimation Performance
What the sources say
- Not checked yetcites [39]
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In the paper
“This new dataset is a more realistic and challenging benchmark for depth estimation, as it is diverse and captures precise structure across images (30k points per frame) at longer ranges (up to 200m vs 80m for previous datasets).”
§ 3D Packing for Self-Supervised Monocular Depth Estimation > DDAD (Dense Depth for Automated Driving).
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In the paper
“and further improves as more complexity is added”
- Also stated in: § Input image · boosted
“improves significantly with more parameters in both cases without overfitting.”
§ 3D Packing for Self-Supervised Monocular Depth Estimation > 5.5. Network Complexity
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- Also stated in: § Input image · boosted
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In the paper
“The ATE results are summarized in Table 6, with our proposed framework achieving competitive results relative to other related methods.”
§ 3D Packing for Self-Supervised Monocular Depth Estimation > A. Pose evaluation
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In the paper
“We use the architecture proposed by [53] without the explainability mask, which we found not to improve results.”
§ 3D Packing for Self-Supervised Monocular Depth Estimation > Depth Network.
What the sources say
- Not checked yetcites [53] source
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In the paper
“It includes a highresolution, long-range Luminar-H2foot_0 as the LiDAR sensor used to generate pointclouds, with a maximum range of 250m and sub-1cm range precision.”
§ 3D Packing for Self-Supervised Monocular Depth Estimation > B. Dense Depth for Automated Driving (DDAD)
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In the paper
“It contains 17,050 training and 4,150 evaluation frames with ground-truth depth maps generated from dense LiDAR measurements using the Luminar-H2 sensor.”
§ 3D Packing for Self-Supervised Monocular Depth Estimation > DDAD (Dense Depth for Automated Driving).
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In the paper
“and can be further improved by self-supervised pretraining on other datasets”
§ 3D Packing for Self-Supervised Monocular Depth Estimation > 5.6. Ablation Studies
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In the paper
“Our best results are achieved when injecting both more unlabeled data at training time and processing higher resolution input images, achieving performance comparable to semisupervised [29] and fully supervised [15] methods.”
§ 3D Packing for Self-Supervised Monocular Depth Estimation > 5.3. Depth Estimation Performance
What the sources say
- Not checked yetcites [29]
- Not checked yetcites [15]
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“the proposed architecture (Table 1) reaches around 128M parameters with an inference time of 60ms on a Titan V100 GPU”
§ 3D Packing for Self-Supervised Monocular Depth Estimation > 5.5. Network Complexity
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In the paper
“From these results we can see that our PackNet-SfM approach significantly outperforms the state-of-the-art [19], based on the ResNet family, the performance gap consistently increasing when larger distances are considered.”
§ 3D Packing for Self-Supervised Monocular Depth Estimation > 5.3. Depth Estimation Performance
What the sources say
- Not checked yetcites [19]
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“DDAD is a cross-continental dataset with scenes drawn from urban settings in the United States (San Francisco Bay Area, Detroit and Ann Arbor) and Japan (Tokyo and Odaiba).”
§ 3D Packing for Self-Supervised Monocular Depth Estimation > B. Dense Depth for Automated Driving (DDAD)
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In the paper
“Additionally, it contains six calibrated cameras time-synchronized at 10 Hz, that together produce a 360 • coverage around the vehicle.”
§ 3D Packing for Self-Supervised Monocular Depth Estimation > B. Dense Depth for Automated Driving (DDAD)
plaindirect- Asserted: passes — The authors put it forward as true.
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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.
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
“Note that in the case of monocular SfM both depth and pose are estimated up to an unknown scale factor, due to the inherent ambiguity of the photometric loss.”
§ 3D Packing for Self-Supervised Monocular Depth Estimation > 3. Self-Supervised Scale-Aware SfM
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In the paper
“This additional velocity loss allows the pose network to make metrically accurate predictions, subsequently resulting in the depth network also learning metrically accurate estimates to maintain consistency (cf.Section 5.4).”
§ 3D Packing for Self-Supervised Monocular Depth Estimation > Velocity Supervision Loss.
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It may state two separate claims as one.
- Rests on an assumption: Instantaneous velocity measurements are ubiquitous in most mobile systems today.
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 evidence that it does not simply memorize training data but learns transferable discriminative features.”
§ 3D Packing for Self-Supervised Monocular Depth Estimation > 5.7. Generalization Capability
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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
“The 3D inductive bias in PackNet enables it to scale with input resolution and number of parameters without overfitting, generalizing better on out-of-domain data such as the NuScenes dataset.”
§ 3D Packing for Self-Supervised Monocular Depth Estimation > Abstract
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None of the sources cited for this claim have been checked yet.
In the paper
“As mentioned in [15,19], ResNet architectures highly benefit from ImageNet pretraining, since they were originally developed for classification tasks.”
§ 3D Packing for Self-Supervised Monocular Depth Estimation > 5.6. Ablation Studies
What the sources say
- Not checked yetcites [15]
- Not checked yetcites [19]
boosteddirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper relies on this without support.
Instantaneous velocity measurements are ubiquitous in most mobile systems today.
“Since instantaneous velocity measurements are ubiquitous in most mobile systems today, we show that they can be directly incorporated in our self-supervised objective to learn a metrically accurate and scale-aware monocular depth estimator.”
The paper relies on this without support.
The same pixel will not be occluded or out-of-bounds in all context images, and the association with minimal photometric loss is correct.
“The intuition is that the same pixel will not be occluded or out-of-bounds in all context images, and that the association with minimal photometric loss should be the correct one.”
The paper relies on this without support.
Depth maps made by projecting LiDAR pointclouds onto the camera frame are accurate enough to serve as ground truth for evaluating monocular depth estimation.
“The depth maps generated from projecting these Luminar pointclouds onto the camera frame allow us to evaluate depth estimation methods in a much more challenging way, both in terms of denseness and longer ranges.”
“At test-time, all monocular methods (M) scale estimated depths with median ground-truth LiDAR information.”
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 cited source says what the claim attributes to it.
In the citing paper
“some methods incorporate more 3D computations into our object detection pipeline by applying a 3D reconstruction method like [4, 5, 6] to create a pseudo-LiDAR or range input of the scene from camera images.”
From DETR3D: 3D Object Detection from Multi-view Images via 3D-to-2D Queries (2021) · cites this paper as [6]
In the citing paper
“The dataset provides semantic segmentation labels only for the validation and test (non-public) sets, significantly restricting its use-case.”
From Multimodal semantic segmentation in autonomous driving: A review of current approaches and future perspectives (2022) · cites this paper as [82]
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
§ 3D Packing for Self-Supervised Monocular Depth Estimation > 5.2. Implementation Details
“We use PyTorch [38] with all models trained across 8 Titan V100 GPUs.”
§ 3D Packing for Self-Supervised Monocular Depth Estimation > References
“38. Jeff Michels, Ashutosh Saxena, and Andrew Y Ng. High speed obstacle avoidance using monocular vision and rein- forcement learning.”
Citations
Cites
Deep Residual Learning for Image Recognition
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Adam: A Method for Stochastic Optimization
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ImageNet: A large-scale hierarchical image database
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Image quality assessment: from error visibility to structural similarity
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Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834
(2014) · Cited by 33,419
The 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
(2017) · Cited by 11,625
Image Super-Resolution Using Deep Convolutional Networks
Dong, C., Loy, C. C., He, K., & Tang, X. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2439281 · Cited by 10,105
Vision 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
Research works printed but not linked: 2
- kingma, 0, Glow: Generative flow with invertible 1×1 convolutions, Advances in neural information processing systems
- graham, 2015, Fractional Max-Pooling
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Items 201–300 of 739
Two-in-One Depth: Bridging the Gap Between Monocular and Binocular Self-supervised Depth Estimation
Zhou, Z., & Dong, Q. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00863 · Cited by 20
Recurrent Multiscale Feature Modulation for Geometry Consistent Depth Learning
Zhou, Z., Fan, X., Shi, P., Xin, Y., Duan, D., & Yang, L. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3420165 · Cited by 17
Papa, L., Alati, E., Russo, P., & Amerini, I. (2022) · IEEE Access · doi:10.1109/access.2022.3170425 · Cited by 17
Two Stream Networks for Self-Supervised Ego-Motion EstimationPreprint
Ambruş, R., Guizilini, V., Li, J., Pillai, S., & Gaidon, A. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.01764 · Cited by 16
Self-citationLDA-Mono: A lightweight dual aggregation network for self-supervised monocular depth estimation
Zhao, B., He, H., Xu, H., Shi, P., Hao, X., & Huang, G. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.112552 · Cited by 16
CU-Net: LiDAR Depth-Only Completion With Coupled U-Net
Wang, Y., Dai, Y., Liu, Q., Yang, P. X., Sun, J., & Li, B. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3201193 · Cited by 16
Yu, K., Li, H., Xing, L., Wen, T., Fu, D., Yang, Y., Zhou, C., Chang, R., Zhao, S., Xing, L., et al. (2023) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2023.10.010 · Cited by 18
MonoVAN: Visual Attention for Self-Supervised Monocular Depth Estimation
Indyk, I., & Makarov, I. (2023) · Proceedings - International Symposium on Mixed and Augmented Reality, ISMAR · doi:10.1109/ismar59233.2023.00138 · Cited by 16
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
Kick Back & Relax: Learning to Reconstruct the World by Watching SlowTV
Spencer, J., Hadfield, S., Russell, C., & Bowden, R. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01445 · Cited by 19
Bello, J. L. G., Moon, J.-H., & Kim, M. (2024) · IEEE Transactions on Image Processing · doi:10.1109/tip.2024.3374045 · Cited by 18
RGB Guided ToF Imaging System: A Survey of Deep Learning-Based Methods
Qiao, X., Poggi, M., Deng, P., Hao, W., Ge, C., & Mattoccia, S. (2024) · International Journal of Computer Vision · doi:10.1007/s11263-024-02089-5 · Cited by 20
Triplane-Smoothed Video Dehazing with CLIP-Enhanced Generalization
Ren, J., Chen, H., Tian, Y., Wu, H., & Zhu, L. (2024) · International Journal of Computer Vision · doi:10.1007/s11263-024-02161-0 · Cited by 16
Hierarchical Temporal Context Learning for Camera-Based Semantic Scene Completion
Li, B., Deng, J., Zhang, W., Liang, Z., Du, D., Jin, X., & Zeng, W. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73235-5_8 · Cited by 17
Beyond Learning: Back to Geometric Essence of Visual Odometry via Fusion-Based Paradigm
Jin, Y., Wei, N., Fu, M., Liu, H., & Zhang, W. (2021) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2021.3121483 · Cited by 16
Online Performance Prediction of Perception DNNs by Multi-Task Learning With Depth Estimation
Klingner, M., & Fingscheidt, T. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3054437 · Cited by 16
Htun, S. N. N., & Fukuda, K. (2024) · Information · doi:10.3390/info15100645 · Cited by 15
Self-supervised monocular depth estimation via multiple bilateral consistency
Lu, Z., & Chen, Y. (2025) · Multimedia Systems · doi:10.1007/s00530-025-01700-0 · Cited by 17
CrossDTR: Cross-view and Depth-guided Transformers for 3D Object Detection
Tseng, C.-Y., Chen, Y.-R., Lee, H.-Y., Wu, T.-H., Chen, W.-C., & Hsu, W. H. (2023) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48891.2023.10161451 · Cited by 15
Contrastive Learning for Depth Prediction
Fan, R., Poggi, M., & Mattoccia, S. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00325 · Cited by 15
SPTR: Structure-Preserving Transformer for Unsupervised Indoor Depth Completion
Zhao, L., Zheng, W., Duan, Y., Zhou, J., & Lu, J. (2023) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2023.3312664 · Cited by 15
THE Benchmark: Transferable Representation Learning for Monocular Height Estimation
Xiong, Z., Huang, W. X., Hu, J., & Zhu, X. X. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3311764 · Cited by 29
Self-supervised deep monocular visual odometry and depth estimation with observation variation
Zhao, W., Wang, Y., Wang, Z., Li, R., Xiao, P., Wang, J., & Guo, R. (2023) · Displays · doi:10.1016/j.displa.2023.102553 · Cited by 15
Towards Better Data Exploitation in Self-Supervised Monocular Depth Estimation
Liu, J., Kong, L., Yang, J., & Liu, W. (2023) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2023.3337594 · Cited by 15
MAMo: Leveraging Memory and Attention for Monocular Video Depth Estimation
Yasarla, R., Cai, H., Jeong, J., Shi, Y., Garrepalli, R., & Porikli, F. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00804 · Cited by 15
Moving SLAM: Fully Unsupervised Deep Learning in Non-Rigid Scenes
Xu, D., Vedaldi, A., & Henriques, J. F. (2021) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros51168.2021.9636075 · Cited by 15
Mixed-Scale Unet Based on Dense Atrous Pyramid for Monocular Depth Estimation
Yang, Y., Wang, Y., Zhu, C., Zhu, M. Q., Sun, H., & Yan, T. (2021) · IEEE Access · doi:10.1109/access.2021.3104605 · Cited by 14
Hu, H., Yang, B., Qiao, Z., Liu, S., Zhu, J., Liu, Z., Ding, W., Zhao, D. X., & Wang, H. (2023) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros55552.2023.10341917 · Cited by 17
Distilling Monocular Foundation Model for Fine-grained Depth Completion
Liang, Y., Hu, Y., Shao, W., & Fu, Y. (2025) · 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/cvpr52734.2025.02073 · Cited by 14
SENSE: Self-Evolving Learning for Self-Supervised Monocular Depth Estimation
Li, G., Huang, R., Li, H., You, Z., & Chen, W. (2023) · IEEE Transactions on Image Processing · doi:10.1109/tip.2023.3338053 · Cited by 15
A Comprehensive Framework for 3D Occupancy Estimation in Autonomous Driving
Gan, W., Mo, N., Xu, H., & Yokoya, N. (2024) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2024.3403134 · Cited by 44
SLAM Based on Camera-2D LiDAR Fusion
Lu, G. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10611546 · Cited by 15
Pan, T., Cao, Z., & Wang, L. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10610921 · Cited by 17
EDS-Depth: Enhancing Self-Supervised Monocular Depth Estimation in Dynamic Scenes
Yu, S., Wu, M., Lam, S.-K., Wang, C., & Wang, R. (2025) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2025.3529999 · Cited by 16
Monocular Depth Estimation for Soft Visuotactile Sensors
Ambruş, R., Guizilini, V., Kuppuswamy, N. S., Beaulieu, A., Gaidon, A., & Alspach, A. N. (2021) · IEEE International Conference on Soft Robotics (RoboSoft) · doi:10.1109/robosoft51838.2021.9479234 · Cited by 13
Self-citationSelf-supervised monocular depth estimation based on image texture detail enhancement
Li, Y., Luo, F., Li, W., Zheng, S., Wu, H., & Xiao, C. (2021) · The Visual Computer · doi:10.1007/s00371-021-02206-2 · Cited by 14
Kumar, V. R., Yogamani, S. K., Milz, S., & Mäder, P. (2021) · Electronic Imaging · doi:10.2352/issn.2470-1173.2021.17.avm-181 · Cited by 14
Self-Supervised 3D Keypoint Learning for Ego-motion Estimation
Tang, J., Ambrus, R., Guizilini, V., Pillai, S., Kim, H., Jensfelt, P., & Gaidon, A. (2019) · arXiv · doi:10.48550/arxiv.1912.03426 · Cited by 14
Self-citationRayzer: a Self-Supervised Large View Synthesis Model
Jiang, H., Tan, H., Wang, P., Jin, H., Zhao, Y., Bi, S., Zhang, K., Luan, F., Sunkavalli, K., Huang, Q., et al. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.00468 · Cited by 50
Unsupervised Monocular Depth Perception: Focusing on Moving Objects
Jiang, H., Ding, L., Sun, Z., & Huang, R. (2021) · IEEE Sensors Journal · doi:10.1109/jsen.2021.3109266 · Cited by 13
Focal-WNet: An Architecture Unifying Convolution and Attention for Depth Estimation
Manimaran, G., & Swaminathan, J. (2022) · IEEE International Conference for Convergence in Technology (I2CT) · doi:10.1109/i2ct54291.2022.9824488 · Cited by 14
Challenges in Low Infrastructure and Off-Road Automated Driving
Carruth, D. W., Walden, C. T., Goodin, C. T., & Fuller, S. C. (2022) · International Conference on Connected and Autonomous Driving (MetroCAD) · doi:10.1109/metrocad56305.2022.00008 · Cited by 16
Two-Stream Based Multi-Stage Hybrid Decoder for Self-Supervised Multi-Frame Monocular Depth
Long, Y., Yu, H., & Liu, B. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3214787 · Cited by 13
DNA-Depth: A Frequency-Based Day-Night Adaptation for Monocular Depth Estimation
Shen, M., Wang, Z., Su, S., Liu, C., & Chen, Q. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3322498 · Cited by 13
Bird-View 3D Reconstruction for Crops with Repeated Textures
Lu, G. (2023) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros55552.2023.10341478 · Cited by 14
3D Distillation: Improving Self-Supervised Monocular Depth Estimation on Reflective Surfaces
Shi, X., Dikov, G., Reitmayr, G., Kim, T., & Ghafoorian, M. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00838 · Cited by 15
Self-Supervised Monocular Depth Estimation With Dual-Path Encoders and Offset Field Interpolation
Feng, C., Zhang, C., Chen, Z., Hu, W., Lü, K., & Ge, L. (2025) · IEEE Transactions on Image Processing · doi:10.1109/tip.2025.3533207 · Cited by 16
VINS-Motion: Tightly-coupled Fusion of VINS and Motion Constraint
Yu, Z., Zhu, L., & Lu, G. (2021) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48506.2021.9562103 · Cited by 13
Semi-Supervised Learning with Mutual Distillation for Monocular Depth Estimation
Baek, J., Kim, G., & Kim, S. (2022) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra46639.2022.9811802 · Cited by 16
Self-supervised coarse-to-fine monocular depth estimation using a lightweight attention module
Li, Y., Luo, F., & Xiao, C. (2022) · Computational Visual Media · doi:10.1007/s41095-022-0279-3 · Cited by 13
Zheng, Q., Yu, T., & Wang, F. (2023) · Computers & Graphics · doi:10.1016/j.cag.2023.01.016 · Cited by 13
DepthInSpace: Exploitation and Fusion of Multiple Video Frames for Structured-Light Depth Estimation
Johari, M. M., Carta, C., & Fleuret, F. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00598 · Cited by 14
Gao, F., Yu, J., Shen, H., Wang, Y., & Yang, H. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.09369 · Cited by 15
Predict to Detect: Prediction-guided 3D Object Detection using Sequential Images
Kim, S., Kim, Y.-S., Lee, I.-J., & Kum, D. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01655 · Cited by 12
Resolution-sensitive self-supervised monocular absolute depth estimation
Zhou, Y., Zhang, C., Deng, L., Fu, J., Li, H., Xu, Z., & Zhang, J. (2024) · Applied Intelligence · doi:10.1007/s10489-024-05414-0 · Cited by 13
Graph semantic information for self-supervised monocular depth estimation
Zhang, D., Wang, C., Wang, H., & Fu, Q. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.110770 · Cited by 13
Unsupervised Scale Network for Monocular Relative Depth and Visual Odometry
Wang, Z., & Chen, Q. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3451584 · Cited by 13
Supervised Object-Specific Distance Estimation from Monocular Images for Autonomous Driving
Davydov, Y., Chen, W.-H., & Lin, Y. (2022) · Sensors · doi:10.3390/s22228846 · Cited by 12
Photo-realistic Neural Domain Randomization
Zakharov, S., Ambruş, R., Guizilini, V., Kehl, W., & Gaidon, A. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19806-9_18 · Cited by 12
Self-citationDepth Field Networks For Generalizable Multi-view Scene Representation
Guizilini, V., Vasiljevic, I., Fang, J., Ambru, R., Shakhnarovich, G., Walter, M. R., & Gaidon, A. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19824-3_15 · Cited by 14
Self-citationThe Second Monocular Depth Estimation Challenge
Spencer, J., Qian, C. S., Trescakova, M., Russell, C., Hadfield, S., Graf, E. W., Adams, W. J., Schofield, A. J., Elder, J., Bowden, R., et al. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00308 · Cited by 13
Object Discovery from Motion-Guided Tokens
Bao, Z., Tokmakov, P., Wang, Y.-X., Gaidon, A., & Hebert, M. (2023) · 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/cvpr52729.2023.02200 · Cited by 13
Self-citationJoint Learning of Image Deblurring and Depth Estimation Through Adversarial Multi-Task Network
Hou, S., Fu, M., & Song, W. (2023) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2023.3279981 · Cited by 11
Yang, S., Wang, Z., Yu, G., Zhou, B., Chen, P., Wang, S., & Zhang, Q. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3284958 · Cited by 11
Self-Supervised Event-Based Monocular Depth Estimation Using Cross-Modal Consistency
Zhu, J., Liu, L., Jiang, B., Wen, F., Zhang, H., Li, W., & Liu, Y. (2023) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros55552.2023.10342434 · Cited by 15
Dhrafani, D., Liu, Y., Jong, A., Shin, U., He, Y., Harp, T., Hu, Y., Oh, J., & Scherer, S. (2025) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2025.3536278 · Cited by 11
A Comprehensive Evaluation of Monocular Depth Estimation Methods in Low-Altitude Forest Environment
Jia, J., Kang, J., Chen, L., Gao, X., Zhang, B., & Yang, G. (2025) · Remote Sensing · doi:10.3390/rs17040717 · Cited by 12
A Framework for Autonomous UAV Navigation Based on Monocular Depth Estimation
Gaigalas, J., Perkauskas, L., Gricius, H., Kanapickas, T., & Kriščiūnas, A. (2025) · Drones · doi:10.3390/drones9040236 · Cited by 11
Yoneyama, R., Duran, A. J., & del Pobil, A. P. (2021) · Sensors · doi:10.3390/s21041437 · Cited by 11
Fang, J., & Liu, G. (2022) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2022.3152241 · Cited by 11
Adaptive Cost Volume Fusion Network for Multi-Modal Depth Estimation in Changing Environments
Park, J., Jeong, Y., Joo, K., Cho, D., & Kweon, I. S. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3150868 · Cited by 11
Self-Supervised Monocular Depth and Motion Learning in Dynamic Scenes: Semantic Prior to Rescue
Lee, S., Rameau, F., Im, S., & Kweon, I. S. (2022) · International Journal of Computer Vision · doi:10.1007/s11263-022-01641-5 · Cited by 11
RAFM: Recurrent Atrous Feature Modulation for Accurate Monocular Depth Estimating
Fan, X., Zhou, Z., Shi, P., Xin, Y., & Zhou, X. (2022) · IEEE Signal Processing Letters · doi:10.1109/lsp.2022.3189597 · Cited by 11
Park, H., & Park, S.-C. (2023) · Electronics · doi:10.3390/electronics12173730 · Cited by 10
Sun, G., Liu, J., Liu, M., Liu, M., & Zhang, Y. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.108790 · Cited by 10
High-Precision Self-supervised Monocular Depth Estimation with Rich-Resource Prior
Han, W., & Shen, J. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72751-1_9 · Cited by 11
Rectified self-supervised monocular depth estimation loss for nighttime and dynamic scenes
Qin, X., Wang, L., Zhu, Y., Fan, M., Zhang, X., He, C., & Dong, Q. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.110026 · Cited by 11
Semisupervised learning-based depth estimation with semantic inference guidance
Zhang, Y., Xiaopeng Fan, B., & Zhao, D. (2022) · Science China Technological Sciences · doi:10.1007/s11431-021-1948-3 · Cited by 10
Dalai, R., Dalai, N., & Senapati, K. K. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-14615-7 · Cited by 11
Scale-Aware Visual-Inertial Depth Estimation and Odometry Using Monocular Self-Supervised Learning
Lee, C., Kim, C., Kim, P., Lee, H., & Kim, H. J. (2023) · IEEE Access · doi:10.1109/access.2023.3252884 · Cited by 10
Chang, R., Yu, K., & Yang, Y. (2023) · Remote Sensing · doi:10.3390/rs15133275 · Cited by 13
Gated Stereo: Joint Depth Estimation from Gated and Wide-Baseline Active Stereo Cues
Walz, S., Bijelic, M., Ramazzina, A., Walia, A., Mannan, F., & Heide, F. (2023) · 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/cvpr52729.2023.01273 · Cited by 12
HQDec: Self-Supervised Monocular Depth Estimation Based on a High-Quality Decoder
Wang, F., & Cheng, J. S. (2023) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2023.3312721 · Cited by 11
Li, W., Hayashi, Y., Oda, M., Kitasaka, T., Misawa, K., & Mori, K. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43996-4_41 · Cited by 10
Revisiting Depth Completion from a Stereo Matching Perspective for Cross-domain Generalization
Bartolomei, L., Poggi, M., Conti, A., Tosi, F., & Mattoccia, S. (2024) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv62453.2024.00127 · Cited by 10
Mind The Edge: Refining Depth Edges in Sparsely-Supervised Monocular Depth Estimation
Talker, L., Cohen, A., Yosef, E., Dana, A., & Dinerstein, M. (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/cvpr52733.2024.01009 · Cited by 14
AugUndo: Scaling Up Augmentations for Monocular Depth Completion and Estimation
Wu, Y., Liu, T. Y., Park, H., Soatto, S., Lao, D., & Wong, A. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73039-9_16 · Cited by 17
Recognizing New Classes with Synthetic Data in the Loop: Application to Traffic Sign Recognition
Villalonga, G., van de Weijer, J., & López, A. (2020) · Italian National Conference on Sensors · doi:10.3390/s20030583 · Cited by 10
Toward Mass Video Data Analysis: Interactive and Immersive 4D Scene Reconstruction
Kraus, M., Pollok, T., Miller, M., Kilian, T., Moritz, T., Schweitzer, D., Beyerer, J., Keim, D. A., Qu, C., & Jentner, W. (2020) · Sensors · doi:10.3390/s20185426 · Cited by 11
Radu, V., Nan, M., Trăşcău, M., Iancu, D.-T., Ghiță, A. Ș., & Florea, A. M. (2021) · International Conference on Control Systems and Computer Science · doi:10.1109/cscs52396.2021.00028 · Cited by 10
Self-supervised Monocular Trained Depth Estimation Using Triplet Attention and Funnel Activation
Xiang, X., Kong, X., Qiu, Y., Zhang, K., & Lv, N. (2021) · Neural Processing Letters · doi:10.1007/s11063-021-10608-5 · Cited by 10
Joint Soft–Hard Attention for Self-Supervised Monocular Depth Estimation
Fan, C., Yin, Z., Xu, F., Chai, A., & Zhang, F. (2021) · Sensors · doi:10.3390/s21216956 · Cited by 10
Revisiting PatchMatch Multi-View Stereo for Urban 3D Reconstruction
Orsingher, M., Zani, P., Medici, P., & Bertozzi, M. (2022) · IEEE Intelligent Vehicles Symposium · doi:10.1109/iv51971.2022.9827259 · Cited by 10
Deep Underwater Monocular Depth Estimation with Single-Beam Echosounder
Liu, H., Roznere, M., & Li, A. Q. (2023) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48891.2023.10161439 · Cited by 10
Zhang, Y., Xu, W., & Lyu, C. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.107793 · Cited by 9
On Robust Cross-view Consistency in Self-supervised Monocular Depth Estimation
Zhao, H., Zhang, J., Chen, Z., Yuan, B., & Tao, D. (2024) · Machine Intelligence Research · doi:10.1007/s11633-023-1474-0 · Cited by 10
Zhang, G., Tang, X., Wang, L., Cui, H., Fei, T., Tang, H., & Jiang, S. (2024) · Complex & Intelligent Systems · doi:10.1007/s40747-024-01575-0 · Cited by 11
Mining Supervision for Dynamic Regions in Self-Supervised Monocular Depth Estimation
Nguyen, H. C., Wang, T., Álvarez, J. M., & Liu, M. (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/cvpr52733.2024.00994 · Cited by 11
DeCoTR: Enhancing Depth Completion with 2D and 3D Attentions
Shi, Y., Singh, M. K., Cai, H. N., & Porikli, F. (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/cvpr52733.2024.01021 · Cited by 9
Metadata
- Authors
- Guizilini, V., Ambrus, R., Pillai, S., Raventos, A., & Gaidon, A.
- Affiliations
- Toyota Research Institute (US)
- Venue
- 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2020-06
- Pages
- 2482-2491
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2991840778
- Semantic Scholar
- 43fcb6cf679cd2ae412d7cb53e85312600cc20dd
- Cited by
- 741
- References
- 59
- Citations by year
- 2026: 51 · 2025: 108 · 2024: 140 · 2023: 155 · 2022: 139 · 2021: 125 · 2020: 19
- FWCI
- 33.78
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Robotics and Sensor-Based Localization
- Keywords
- monocular depth estimation, self-supervised learning, 3D convolution, out-of-domain generalization, real-time depth estimation, autonomous driving, KITTI dataset, nuScenes
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-08, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-08 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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