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
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
“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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In the paper
“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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- Based: unclear — It is unclear why the paper says it holds.
- 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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In the paper
“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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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
“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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In the paper
“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]
hedgeddirect- Asserted: passes — The authors put it forward as true.
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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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Probably not a substantial claim. The paper’s conclusions may not depend on it.
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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
hedged- Asserted: passes — The authors put it forward as true.
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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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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
“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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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
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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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Probably not a substantial claim.
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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
plain- Asserted: passes — The authors put it forward as true.
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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
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
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
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 601–700 of 739
Peer Review #1 of "Self-supervised recurrent depth estimation with attention mechanisms (v0.1)"
(2022) · PeerJ Computer Science · doi:10.7287/peerj-cs.865v0.1/reviews/1
Peer Review #1 of "Self-supervised recurrent depth estimation with attention mechanisms (v0.3)"
(2022) · PeerJ Computer Science · doi:10.7287/peerj-cs.865v0.3/reviews/1
Jiang, X., Chen, X., & Zhang, Z. (2021) · International Conference on Video and Image Processing (ICVIP) · doi:10.1145/3511176.3511184
Improved DNN Robustness by Multi-task Training with an Auxiliary Self-Supervised Task
Klingner, M., & Fingscheidt, T. (2022) · Springer eBooks · doi:10.1007/978-3-031-01233-4_5
Efficient View Clustering and Selection for City-Scale 3D Reconstruction
Orsingher, M., Zani, P., Medici, P., & Bertozzi, M. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-06430-2_10
Research on 3D Object Detection Method Based on Vlidar
Sun, Y., & Hou, Z. (2022) · 2022 3rd International Conference on Computer Vision, Image and Deep Learning & International Conference on Computer Engineering and Applications (CVIDL & ICCEA) · doi:10.1109/cvidliccea56201.2022.9825390
Zhao, J., Zhao, C., Liu, C., Zhang, C., & Zhang, W. (2022) · Journal of Electronic Imaging · doi:10.1117/1.jei.31.5.053013
ARO-DeepSFM: deep structure-from-motion with alternating recursive optimization
Cui, R., & huang, H. (2022) · International Conference on Digital Image Processing · doi:10.1117/12.2644363
3D Object Aided Self-Supervised Monocular Depth Estimation
Wei, S., Chen, G., Chi, W., Wang, Z., & Sun, L. (2022) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros47612.2022.9981590 · Cited by 1
Pseudo Depth Maps for RGB-D SLAM
Zhang, Y., Wu, T., Zhao, H., Du, S., Yu, L., & Wang, X. (2022) · International Conference on High Performance Big Data and Intelligent Systems (HPBD&IS) · doi:10.1109/hdis56859.2022.9991394
Restoring Information With Pyramid Recepting Block in Monocular Depth Estimation
Liu, T., & Yu, Y. (2022) · IEEE International Conference on Frontiers Technology of Information and Computer (ICFTIC) · doi:10.1109/icftic57696.2022.10075136
wildNeRF: Novel view synthesis of in-the-wild dynamic scenes using using sparse monocular view dataPreprint
Khalid, S., & Rudzicz, F. (2023) · Research Square · doi:10.21203/rs.3.rs-2459997/v1
Online Consistent Video Depth with Gaussian Mixture Representation
Liu, C., Eckart, B., & Kautz, J. (2023) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48891.2023.10160785
Geometric-Aware Calibration Mechanism for Self-Supervised Depth Estimation
Wang, Y., Li, G., Zhang, R., Chen, X., & Li, T. H. (2022) · IEEE Smart World Congress (SWC) · doi:10.1109/smartworld-uic-atc-scalcom-digitaltwin-pricomp-metaverse56740.2022.00069
Detail-Preserving Self-Supervised Monocular Depth with Self-Supervised Structural Sharpening
Bello, J. L. G., Moon, J.-H., & Kim, M. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00031
wildNeRF: Novel view synthesis of in-the-wild dynamic scenes using sparse monocular dataPreprint
Khalid, S., & Rudzicz, F. (2023) · Research Square · doi:10.21203/rs.3.rs-3320310/v1
Adaptive Momentum-Based Loss Rebalancing for Monocular Depth Estimation
Yu, W.-G., & Heo, Y. S. (2023) · IEEE Access · doi:10.1109/access.2023.3325392
UWDepth:Underwater image Depth estimation
Zhou, J., Wang, S., & Jiang, X. (2023) · IEEE Information Technology and Mechatronics Engineering Conference (ITOEC) · doi:10.1109/itoec57671.2023.10291879
Night vision self-supervised Reflectance-Aware Depth Estimation based on reflectance
Yao, Y., Pu, F., Chen, H., Tang, R., Li, J., & Xu, X. (2023) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2023.103962
Object Detection with Depth Information in Road Scenes
Liu, R., Chen, X., & Tao, B. (2023) · Communications in computer and information science · doi:10.1007/978-981-99-8021-5_15
MRA-Depth: Multi-Resolution Attention based Monocular Depth Estimation
Wen, J. A., & Niu, L. (2023) · International Conference on Robotics and Computer Vision (ICRCV) · doi:10.1109/icrcv59470.2023.10329023
QuaFedAsync: Quality-based Asynchronous Federated Learning for the Embedded Systems
Zhang, H., Bosch, J., & Olsson, H. H. (2023) · Euromicro Conference on Software Engineering and Advanced Applications (SEAA) · doi:10.1109/seaa60479.2023.00019
Monocular depth estimation via cross-spectral stereo information fusion
Liu, H. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-023-17966-3
Multi-scale Depth-guided Instance Segmentation Based on Attention Mechanism
Han, Y., Cao, X., Tian, W., & Yu, X. (2023) · CAA International Conference on Vehicular Control and Intelligence (CVCI) · doi:10.1109/cvci59596.2023.10397278
Optical-Flow-Aided Self-Supervised Single View Depth Estimation from Monocular Videos
Bello, J. L. G., & Kim, M. (2023) · IEEE International Conference on Visual Communications and Image Processing (VCIP) · doi:10.1109/vcip59821.2023.10402650
Xiang, X., Yan, Q., Qiao, Y., & Saddik, A. E. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4768854
Rfdepth: A Self-Supervised Monocular Depth Estimation Method Driven by Explicit Robust FeaturePreprint
Wu, C., Li, X., Kong, D., Che, H., Liu, X., & Jiang, T. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4777721
Shyam, P., Okon, A., & Yoo, H. (2024) · IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW) · doi:10.1109/wacvw60836.2024.00030
Wen, T., Tang, H., Sun, G., & Zhang, L. (2024) · Research Square · doi:10.21203/rs.3.rs-4113240/v1
An AI Driven Approach for Smoke and Haze Elimination Algorithm
Akash, R., Raja Abilash, J., & Gowthaman, R. (2024) · International Journal of Research Publication and Reviews · doi:10.55248/gengpi.5.0524.1423
Hou, K., Shi, S., Gao, J., Long, J., Gai, X., Zhang, X., Liu, Y., Zhang, J., Sun, H., & Zhang, K. (2024) · Mechanisms and machine science · doi:10.1007/978-981-97-0922-9_123
Li, J., Wang, Z., & Sun, K. (2024) · International Conference on Electronic Engineering and Information Systems (EEISS) · doi:10.1109/eeiss62553.2024.00016
Yang, C., Lu, Y., Qiu, Y., & Wang, Y. (2024) · Optical Engineering · doi:10.1117/1.oe.63.8.088103
TSFF: a two-stage fusion framework for 3D object detection
Jiang, G., Li, S., Huang, Z., Cai, G., & Su, J. (2024) · PeerJ Computer Science · doi:10.7717/peerj-cs.2260
Multimodal Monocular Dense Depth Estimation with Event-Frame Fusion Using Transformer
Xiao, B., Xu, J., Zhang, Z., Xing, T., Wang, J., & Ren, Y. S. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72335-3_29
SAFESUB: Safe and Autonomous Subsea Intervention
Transeth, A. A., Thorstensen, J., Mohammed, A. K., Thielemann, J. T., Ening, K., Grøtli, E. I., Haugaløkken, B. O. A., Brandt, M. A., Møller, M. T., Hovland, R. P., et al. (2024) · IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications · doi:10.1109/mesa61532.2024.10704871
RePLAy: Remove Projective LiDAR Depthmap Artifacts via Exploiting Epipolar Geometry
Zhu, S., Ganesan, G. C., Kumar, A., & Liu, X. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73004-7_23
MCC-DEM: Multi-Scale Context Constraint-Based Depth Estimation Method for Monocular Camera
Fang, C., Xue, X., Deng, G., Zhang, C., Xu, Y., Cao, H., Xu, Y., Wang, F., & Bao, L. (2024) · IEEE International Conference on Information Systems and Computer Aided Education (ICISCAE) · doi:10.1109/iciscae62304.2024.10761276
SEDiff: Structure Extraction for Domain Adaptive Depth Estimation via Denoising Diffusion Models
Shim, D., & Kim, H. J. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72655-2_3
MGNiceNet: Unified Monocular Geometric Scene Understanding
Schön, M., Buchholz, M., & Dietmayer, K. (2024) · Lecture notes in computer science · doi:10.1007/978-981-96-0966-6_20
DepthSegNet24: A Label-Free Model for Robust Day-Night Depth and Semantics
Thanh, P. T. H., Nguyen, T. H., Nguyen, M. H. V., Tran, T. T., Pham, T. V., Nguyen, D. D., Duy, T. V. T., & Naotake, N. (2024) · Lecture notes in computer science · doi:10.1007/978-981-96-0963-5_3
Multi-scale Feature Distribution Prediction Decoder for Lightweight Monocular Depth Estimation
Ma, X., Ji, Y., Song, C., & Zhong, S. (2024) · International Conference on New Trends in Computational Intelligence (NTCI) · doi:10.1109/ntci64025.2024.10776079
Pan, X., Du, J., Su, S., Zong, W., Wang, X., Liu, C., & Chen, Q. (2024) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros58592.2024.10802337
Hou, S., Song, W., Wang, R., Wang, M., Yang, Y., & Fu, M. (2024) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros58592.2024.10801331
Optimizing Depth and Ego-Motion Estimation with Swin Transformer: A Comprehensive Study
Hossain, M. S., Hasan, S. M. M., Mamun, M. A., Srizon, A. Y., Faruk, M. F., & Hossain, M. R. (2024) · IEEE International Conference on Power, Electrical, Electronics and Industrial Applications (PEEIACON) · doi:10.1109/peeiacon63629.2024.10800482
SIE-DepthNet: Semantic-Guided Monocular Depth Estimation for Dynamic Environment
Song, Z., Gao, Y., Dai, S., Li, S., Hao, A., & Zhang, S. (2025) · Lecture notes in computer science · doi:10.1007/978-981-96-3679-2_13
EnIter: Enhancing Iterative Multi-View Depth Estimation with Universal Contextual Hints
Du, Q., Yin, H., Nie, L., Liu, Y., & Wan, J. (2025) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3731760
Multiscale Spatio-Temporal Fusion Network for Video DehazingPreprint
Zhang, Q., Chen, G., Zhang, Y., Liu, S., & Wang, J. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5245282
EMDepth: Self-Supervised Event-Based Monocular Depth Estimation
Lin, L., Li, K., Lu, Y., & Li, T. (2024) · International Conference on Neuromorphic Computing (ICNC) · doi:10.1109/icnc64304.2024.10987707
Introducing a Class-Aware Metric for Monocular Depth Estimation: An Automotive Perspective
Bader, T. A., Eisemann, L., Pogorzelski, A., Jangid, N., & Kis, A.-B. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-91672-4_17
Self-Supervised Multi-Camera Collaborative Depth Prediction With Latent Diffusion Models
Xu, J., Liu, X., Bai, Y., Jiang, J., & Ji, X. (2025) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2025.3571027 · Cited by 3
DepSAM: Monocular Depth Estimation with SAM in Night-time Scenes
Tian, H., Zhu, C., & Liu, K. (2024) · International Conference on Virtual Reality and Visualization · doi:10.1109/icvrv62410.2024.00017
Yiquan, 吴. W., & Haobo, 谢. X. (2025) · Acta Optica Sinica (Online) · doi:10.3788/aosol250454
Li, Y., Shi, D., Jing, L., Huang, Y., & Yang, S. (2025) · Communications in computer and information science · doi:10.1007/978-981-96-9911-7_6
MonoDeMB: Comprehensive Monocular DepthMap Benchmark
Chopuryan, V., Kuznetsov, M. D., Latonov, V., Mashurov, V., & Semenova, N. (2025) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3711896.3737394
Large-Scale Multiobjective Model Pruning for Intelligent Transport Systems
Cao, B., Du, R. Y., & Wang, Z. (2025) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2025.3591361
ULDepth: Transform Self-Supervised Depth Estimation to Unpaired Multi-Domain Learning
Thanh, P. T. H., Tran, T. T., Nguyen, T. H., Nguyen, M. H. V., Pham, T. V., Duy, T. V. T., & Nguyen, D. D. (2025) · IEEE Open Journal of Signal Processing · doi:10.1109/ojsp.2025.3597873
A Hybrid UNet with Attention and a Perceptual Loss Function for Monocular Depth Estimation
Turkmen, H., & Akgün, D. (2025) · Mathematics · doi:10.3390/math13162567
Wavelet Packing for Self-Supervised Monocular Depth Estimation
Rhim, A., Qin, L., Benmokhtar, R., & Perrotton, X. (2025) · Proceedings - International Conference on Image Processing · doi:10.1109/icip55913.2025.11084432
Human-like monocular depth biases in deep neural networks
Kubota, Y., & Fukiage, T. (2025) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1013020 · Cited by 1
GRIN: Zero-Shot Metric Depth with Pixel-Level Diffusion
Guizilini, V., Tokmakov, P., Dave, A., & Ambruş, R. (2025) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv66043.2025.00016 · Cited by 2
Self-citationTangpornpisit, T., Handayani, L., Chegodaev, D., Raes, R. G. G., & Satou, K. (2025) · IPSJ Transactions on Bioinformatics · doi:10.2197/ipsjtbio.18.14
Dual-Path Attention Network for Lightweight Self-Supervised Monocular Depth Estimation
Zhang, C., Tian, T., Han, C., Shao, T., Zhou, M., & Zhao, S. (2025) · IEEE Sensors Journal · doi:10.1109/jsen.2025.3601212
PRIDEV: A Plug-and-Play Refinement for Improved Depth Estimation in Videos
Xu, J., Liu, H., Wu, J., & Xu, X. (2025) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra55743.2025.11128698
Optical Lens Attack on Deep Learning Based Monocular Depth Estimation
Zhou, C., Yan, Q., Kent, D., Wang, G., Zhang, Z., & Radha, H. (2025) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-031-94445-1_12
Let the Monocular Depth Estimation Model See Further
Guo, G., Liu, X., Fei, J., Li, L., Zhan, X., & Cheng, X. (2025) · IEEE China Conference on System Simulation Technology and its Applications (CCSSTA) · doi:10.1109/ieeeconf65522.2025.11137165
Huang, M., Zhang, X., Mai, Y., Zhang, J., & Zhang, D. (2025) · IEEE International Conference on Electronic Information and Communication Technology (ICEICT) · doi:10.1109/iceict66683.2025.11159965
Li, W., Hayashi, Y., Oda, M., Kitasaka, T., Misawa, K., & Mori, K. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-05114-1_23
Self-Supervised Monocular VO in Dynamic Scenes With Hybrid Masks and Depth Uncertainty
Meng, X., & Sun, B. (2025) · International Conference on Artificial Intelligence and Electromechanical Automation (AIEA) · doi:10.1109/aiea66061.2025.11160516
Pu, Z., Chen, S., Zou, B., & Pu, B. (2023) · Journal of Computer-Aided Design & Computer Graphics · doi:10.3724/sp.j.1089.2023.19299
Dual-Scale Attention Networks for Efficient Monocular Depth Estimation
He, Z., Sun, Z., Yang, J., Du, C., & Xia, Y. (2025) · Chinese Control Conference (CCC) · doi:10.23919/ccc64809.2025.11178391
CCNeXt: An effective self-supervised stereo depth estimation approach
Lopes, A., de Souza, R. M., & Pedrini, H. (2025) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2025.104529
Spatio-Temporally Consistent Depth Estimation for Dynamic Scenes using 3D Scene Flows
Cai, Y., Jing, T., Liu, C., Lian, Z., Huang, S.-S., & Huang, H. (2025) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme59968.2025.11210132
Yu, R., Ma, G., Guo, J. C., & Xu, L. (2025) · Applied Sciences · doi:10.3390/app152211890
Adjacent-view Transformers for Supervised Surround-view Depth Estimation
Guo, X., Yuan, W., Zhang, Y., Tian, Y., Zhang, C., Zhu, Z. D., Zou, Q., & Chen, L. (2025) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros60139.2025.11246758 · Cited by 5
Self-Supervised Geometry-Guided Initialization for Robust Monocular Visual Odometry
Kanai, T., Vasiljevic, I., Guizilini, V., & Shintani, K. (2025) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros60139.2025.11247022 · Cited by 1
Self-citationSelf-supervised Monocular Depth Estimation for Dynamic Objects with Ground Propagation
Li, H., Poggi, M., Tosi, F., & Mattoccia, S. (2025) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros60139.2025.11246123
A Multi-Modal Benchmark for Long-Range Depth Evaluation in Adverse Weather Conditions
Walz, S., Ramazzina, A., Scheuble, D., Brucker, S., Zuber, A., Ritter, W., Bijelic, M., & Heide, F. (2025) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros60139.2025.11246099
Flow-Guided Consistent Video Depth Estimation for Cross-Dataset Generalization
Jang, J., & Kim, C. (2025) · Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) · doi:10.1109/apsipaasc65261.2025.11249115
Jia, S., Wang, Y., Chen, H., & Huang, S. (2025) · Pattern Analysis and Applications · doi:10.1007/s10044-025-01584-w
DepthP+P: Metric Accurate Monocular Depth Estimation using Planar and Parallax
Safadoust, S., & Güney, F. (2025) · Journal of Soft Computing and Artificial Intelligence · doi:10.55195/jscai.1748633
Scale-Invariant and View-Relational Representation Learning for Full Surround Monocular Depth
Hwang, K., Choi, W., Han, K., Choi, W., Choi, M., Na, Y., Park, M., & Im, S. (2025) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2025.3635451
Towards Domain-Invariant Localization and Mapping with Visual Foundation Model
Qi, Z., Cheng, L., Zhou, Z., Guo, J., Wang, B., & Xiong, G. (2025) · IEEE International Conference on Unmanned Systems (ICUS) · doi:10.1109/icus66297.2025.11294685
Hu, L., Wang, S., Hu, J., Yao, X., & Zhang, S. (2026) · Pattern Analysis and Applications · doi:10.1007/s10044-026-01612-3
RaCFusion: Improving Camera-Based 3D Object Detection via Radar-Assisted Hierarchical Refinement
Wang, Y., Deng, J., Hou, Y., Li, Y., Wang, L., & Zhang, Y. (2026) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2026.3655290 · Cited by 1
Multi-Modal Feature Fusion Distance Gating 3D Imaging Based on Edge Computing
Huang, H., Zhou, B., Xie, Y.-A., Lai, P., Zhang, X., & Zhu, J. (2025) · IEEE International Conference on Cloud Computing Technology and Science · doi:10.1109/cloudcom67567.2025.11331355
R-TAFM: purely convolutional recurrent refinement for deployment-oriented monocular depth estimation
Zhou, Z., Fan, X., Shi, P., Xin, Y., Zhang, C., & Zhang, Y. (2026) · Journal of King Saud University - Computer and Information Sciences · doi:10.1007/s44443-026-00477-0
Boosting MonoDepth With Foundation Models and Edge Laplace Cross-Entropy in Remote Sensing
Zhou, H., Xiong, W., Liu, W., Li, X., Xiong, B., Zhu, Z., & Rao, Z. (2026) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2026.3664691
Interpreting Safety: A LLM and STPA Approach
Qi, Y., Chen, S., Yin, X., Ruan, W., Khastgir, S., Ruan, J. S., Zhao, X., & Huang, X. (2026) · Lecture notes in computer science · doi:10.1007/978-981-95-7081-2_1
Gupta, M., Devi, B., & Shankar, V. G. (2025) · International Conference on Intelligent Computing, Information and Control Systems (ICOIICS) · doi:10.1109/icoiics67115.2025.11390199
Depth Estimation for Picking Transparent Objects Using a Polarization Camera
Yamada, K., Kumar, P., Domae, Y., Kiyokawa, T., Wan, W., & Harada, K. (2026) · IEEE/SICE International Symposium on System Integration (SII) · doi:10.1109/sii64115.2026.11404502
FuseNet: Feature Enhancement-Based Multi-Frame Depth Estimation
Ni, R., & Cao, M. (2025) · International Conference on Pattern Recognition and Artificial Intelligence (PRAI) · doi:10.1109/prai67447.2025.11412427
Liu, W., Wang, W., & Meng, J. (2025) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc60802.2025.11423179
DenseRadar: Dense Radar Generation and Point Consistency Aggregation for 3D Object Detection
Wang, J., Xiong, S., Du, C., Li, H., & Xiong, S. (2026) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2026.3678439
Deep learning for light field view synthesis from monocular and a very sparse set of input views
Evain, S. (2021) · theses.fr (ABES) · doi:10.70675/cadb0b5ezeb43z40ffzbc36za1fd0e0ea42f
Chekhmestruk, R., & Voitsekhovska, O. (2026) · ELCVIA Electronic Letters on Computer Vision and Image Analysis · doi:10.5565/rev/elcvia.2276
Loukkal, A. (2021) · theses.fr (ABES) · doi:10.70675/ad721e60z1d82z43ddza56fz45c72f787af1
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.
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