Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescue

ImageNet classification with deep convolutional neural networks

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155

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

Microsoft COCO: Common Objects in Context

Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C. L. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10602-1_48 · Cited by 45,460

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

Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation

Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.81 · Cited by 32,684

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

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

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

(2017) · doi:10.1109/cvpr35066.2017 · Cited by 7,284

International Conference on Learning Representations (ICLR 2013)

松尾, 豊. (2013) · 人工知能学会誌 = Journal of Japanese Society for Artificial Intelligence · Cited by 6,286

Learning Deconvolution Network for Semantic Segmentation

Noh, H., Hong, S., & Han, B. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.178 · Cited by 4,422

Joint prediction of monocular depth and structure using planar and parallax geometry

Xing, H., Cao, Y., Biber, M., Zhou, M., & Burschka, D. (2022) · Pattern Recognition · doi:10.1016/j.patcog.2022.108806 · Cited by 9

Robust Depth Estimation in Foggy Environments Combining RGB Images and mmWave Radar

Xiong, M., Xu, X. W., Yang, D., & Steinbach, E. G. (2022) · IEEE International Symposium on Multimedia (ISM) · doi:10.1109/ism55400.2022.00011 · Cited by 12

A Review of Monocular Depth Estimation Methods Based on Deep Learning

Farooq, H., & Chachoo, M. A. (2023) · International Conference ICT for Digital, Smart and Sustainable Development · doi:10.4108/eai.24-3-2022.2318957 · Cited by 9

Learn to Adapt for Self-Supervised Monocular Depth Estimation

Sun, Q., Yen, G. G., Tang, Y. S., & Zhao, C. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3289051 · Cited by 13

Self-Supervised Monocular Depth Estimation by Digging into Uncertainty Quantification

Li, Y.-Z., Zheng, S., Tan, Z.-X., Cao, T., Luo, F., & Xiao, C. (2023) · Journal of Computer Science and Technology · doi:10.1007/s11390-023-3088-y · Cited by 9

ViTA: Video Transformer Adaptor for Robust Video Depth Estimation

Xian, K., Peng, J., Cao, Z., Zhang, J., & Lin, G. (2023) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2023.3309559 · Cited by 9

Motorcycle Detection and Collision Warning Using Monocular Images from a Vehicle

Shabestari, Z. B., Ahmadabadian, A. H., & Fabio Remondino, A. (2023) · Remote Sensing · doi:10.3390/rs15235548 · Cited by 12

Multi-Object Discovery by Low-Dimensional Object Motion

Safadoust, S., & Güney, F. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00074 · Cited by 11

F 2 Depth: Self-supervised indoor monocular depth estimation via optical flow consistency and feature map synthesis

Guo, X., Zhao, H., Shao, S., Li, X., & Zhang, B. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.108391 · Cited by 10

Edge-Enhanced Dual-Stream Perception Network for Monocular Depth Estimation

Liu, Z., & Wang, Q. (2024) · Electronics · doi:10.3390/electronics13091652 · Cited by 10

CaBins: CLIP-based Adaptive Bins for Monocular Depth Estimation

Son, E., & Lee, S. J. (2024) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00458 · Cited by 10

Unsupervised Learning of Accurate Camera Pose and Depth From Video Sequences With Kalman Filter

Wang, Y., & Xu, Y.-F. (2019) · IEEE Access · doi:10.1109/access.2019.2903871 · Cited by 9

Learning Typical 3D Representation from a Single 2D Correspondence Using 2D-3D Transformation Network

Islam, N. U., & Lee, S. (2019) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-19063-7_35 · Cited by 9

Iterative Refinement of Uniformly Focused Image Set for Accurate Depth from Focus

Salokhiddinov, S., & Lee, S. (2020) · Applied Sciences · doi:10.3390/app10238522 · Cited by 9

DRNet: A Depth-Based Regression Network for 6D Object Pose Estimation

Jin, L., Wang, X., He, M., & Wang, J. (2021) · Sensors · doi:10.3390/s21051692 · Cited by 9

Single View Physical Distance Estimation using Human Pose

Fei, X., Wang, H., Cheong, L. L., Zeng, X., Wang, M., & Tighe, J. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01218 · Cited by 10

RAUM-VO: Rotational Adjusted Unsupervised Monocular Visual Odometry

Cimarelli, C., Bavle, H., Sánchez-López, J. L., & Voos, H. (2022) · Sensors · doi:10.3390/s22072651 · Cited by 9

Solving Monocular Sensors Depth Prediction Using MLP-Based Architecture and Multi-Scale Inverse Attention

Cheng, Z., Zhang, Y., & Tang, C. (2022) · IEEE Sensors Journal · doi:10.1109/jsen.2022.3187152 · Cited by 9

A 3D reconstruction based on an unsupervised domain adaptive for binocular endoscopy

Zhang, G., Huang, Z., Lin, J., Li, Z., Cao, E., Pang, Y., & Sun, W. (2022) · Frontiers in Physiology · doi:10.3389/fphys.2022.994343 · Cited by 9

Single image depth estimation based on sculpture strategy

Chen, S., Fan, X., Pu, Z., Ouyang, J., & Zou, B. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.109067 · Cited by 9

Layered Depth Refinement with Mask Guidance

Kim, S. Y., Zhang, J., Niklaus, S., Fan, Y., Chen, S., Lin, Z., & Kim, M. (2022) · 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/cvpr52688.2022.00383 · Cited by 9

CbwLoss: Constrained Bidirectional Weighted Loss for Self-Supervised Learning of Depth and Pose

Wang, F., Cheng, J. S., & Liu, P. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3250744 · Cited by 9

Diffusion-Augmented Depth Prediction with Sparse Annotations

Li, J., Wang, Y., Huang, Z., Zheng, J., Xian, K., Cao, Z., & Zhang, J. (2023) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3581783.3611807 · Cited by 12

Object Detection and Monocular Stable Distance Estimation for Road Environments: A Fusion Architecture Using YOLO-RedeCa and Abnormal Jumping Change Filter

Lv, H., Du, Y., Ma, Y., & Yuan, Y. (2024) · Electronics · doi:10.3390/electronics13153058 · Cited by 9

Advancing Autonomous Driving: DepthSense With Radar and Spatial Attention

Hussain, M. I., Naz, Z., Rafique, M. A., & Jeon, M. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3493196 · Cited by 13

Unsupervised detail-preserving network for high quality monocular depth estimation

Zhang, M., Ye, X., & Fan, X. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.05.015 · Cited by 9

An Object Context Integrated Network for Joint Learning of Depth and Optical Flow

Zhai, M., Xiang, X., Lv, N., Kong, X., & Saddik, A. E. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2020.3007843 · Cited by 9

SFA-MDEN: Semantic-Feature-Aided Monocular Depth Estimation Network Using Dual Branches

Wang, R., Zou, J., & Wen, J. Z. (2021) · Sensors · doi:10.3390/s21165476 · Cited by 9

Epipolar Geometry based Learning of Multi-view Depth and Ego-Motion from Monocular Sequences

Prasad, V., Das, D., & Bhowmick, B. (2018) · Indian Conference on Computer Vision, Graphics and Image Processing · doi:10.1145/3293353.3293427 · Cited by 9

Recurrent Neural Network for Learning DenseDepth and Ego-Motion from VideoPreprint

Wang, R., Frahm, J., & Pizer, S. M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.06558 · Cited by 9

Geometry meets semantics for semi-supervised monocular depth estimationPreprint

Ramirez, P. Z., Poggi, M., Tosi, F., Mattoccia, S., & Stefano, L. D. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.04093 · Cited by 9

Self-Supervised Learning of Depth and Camera Motion from 360° VideosPreprint

Wang, F.-E., Hu, H.-N., Cheng, H.-T., Lin, J.-T., Yang, S.-T., Shih, M.-L., Chu, H., & Sun, M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.05304 · Cited by 9

EyeNet: A Multi-Task Network for Off-Axis Eye Gaze Estimation and User UnderstandingPreprint

Wu, Z., Rajendran, S., van As, T., Zimmermann, J., Badrinarayanan, V., & Rabinovich, A. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1908.09060 · Cited by 9

Deep Learning based Monocular Depth Prediction: Datasets, Methods and ApplicationsPreprint

Li, Q., Zhu, J., Liu, J., Cao, R., Li, Q., Jia, S., & Qiu, G. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.04123 · Cited by 12

gradSLAM: Automagically differentiable SLAMPreprint

Murthy, J. K., Saryazdi, S., Iyer, G., & Paull, L. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.10672 · Cited by 26

ES-Net: An Efficient Stereo Matching NetworkPreprint

Huang, Z., Norris, T. B., & Wang, P. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.03922 · Cited by 14

An Anti-Collision Algorithm for Self-Organizing Vehicular Ad-Hoc Network Using Deep Learning

Lu, Z., Shu, W., & Yan, 李. L. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3241148 · Cited by 8

PrimeDepth: Efficient Monocular Depth Estimation with a Stable Diffusion Preimage

Zavadski, D., Kalšan, D., & Rother, C. (2024) · Lecture notes in computer science · doi:10.1007/978-981-96-0917-8_2 · Cited by 8

Neural vision-based semantic 3D world modeling

Papadopoulos, S., Mademlis, I., & Pitas, I. (2021) · IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW) · doi:10.1109/wacvw52041.2021.00024 · Cited by 8

Geometric Constraints for Self-supervised Monocular Depth Estimation on Laparoscopic Images with Dual-task Consistency

Li, W., Hayashi, Y., Oda, M., Kitasaka, T., Misawa, K., & Mori, K. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16440-8_45 · Cited by 8

Integration of ontology reasoning-based monocular cues in deep learning modeling for single image depth estimation in urban driving scenarios

Benkirane, F. E., Crombez, N., Ruichek, Y., & Hilaire, V. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.110184 · Cited by 8

Creating a Forensic Database of Shoeprints from Online Shoe-Tread Photos

Shafique, S., Kong, B., Kong, S., & Fowlkes, C. C. (2023) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv56688.2023.00092 · Cited by 8

Exploring the Mutual Influence Between Self-Supervised Single-Frame and Multi-Frame Depth Estimation

Xiang, J., Wang, Y., An, L., Liu, H., & Liu, J. (2023) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2023.3309134 · Cited by 8

Delving Into Multi-Illumination Monocular Depth Estimation: A New Dataset and Method

Liang, Y., Zhang, Z., Xian, C., & He, S. (2024) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2024.3353544 · Cited by 8

Chfnet: a coarse-to-fine hierarchical refinement model for monocular depth estimation

Chen, H., & Wang, Y. (2024) · Machine Vision and Applications · doi:10.1007/s00138-024-01560-0 · Cited by 8

Underground Mapping and Localization Based on Ground-Penetrating Radar

Zhang, J., & Lu, G. (2024) · Lecture notes in computer science · doi:10.1007/978-981-96-0885-0_8 · Cited by 8

Risky Region Localization with Point Supervision

Kozuka, K., & Niebles, J. C. (2017) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2017.38 · Cited by 8

Visual Depth Mapping from Monocular Images using Recurrent Convolutional Neural Networks

Mern, J., Julian, K. D., Tompa, R. E., & Kochenderfer, M. J. (2019) · AIAA SciTech Forum · doi:10.2514/6.2019-1189 · Cited by 8

AsiANet: Autoencoders in Autoencoder for Unsupervised Monocular Depth Estimation

Yusiong, J. P. T., & Naval, P. C. (2019) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2019.00053 · Cited by 8

Self-Supervised Subsea SLAM for Autonomous Operations

Marques, F. M., Costa, P., Castro, F., & Parente, M. A. (2019) · Offshore Technology Conference · doi:10.4043/29602-ms · Cited by 7

DISCO: Depth Inference from Stereo using Context

Swami, K., Raghavan, K., Pelluri, N., Sarkar, R., & Bajpai, P. (2019) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2019.00093 · Cited by 8

Unsupervised learning of depth estimation based on attention model and global pose optimization

Dai, R., Gao, Y., Fang, Z., Jiang, X., Wang, A., Zhang, J., & Zhong, C. (2019) · Signal Processing Image Communication · doi:10.1016/j.image.2019.07.007 · Cited by 8

Urban 3D Structure Reconstruction Through a Generative Adversarial Network Model

Aithal, B. H., Das, S. K., & Prakash, P. S. (2020) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-020-04850-7 · Cited by 8

Imaging from temporal data via spiking convolutional neural networks

Kirkland, P., Kapitány, V., Lyons, A., Soraghan, J. J., Turpin, A., Faccio, D., & Caterina, G. D. (2020) · Emerging Imaging and Sensing Technologies for Security and Defence; and Advanced Manufacturing Technologies for Micro- and Nanosystems in Security and Defence · doi:10.1117/12.2573484 · Cited by 8

OnionNet: Single-View Depth Prediction and Camera Pose Estimation for Unlabeled Video

Gu, T., Wang, Z., Li, D., Yang, H., Du, W., & Zhou, Y. (2020) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2020.3042521 · Cited by 8

Spatiotemporal Guided Self-Supervised Depth Completion from LiDAR and Monocular Camera

Chen, Z., Wang, H., Wu, L., Zhou, Y., & Wu, D. O. (2020) · IEEE International Conference on Visual Communications and Image Processing (VCIP) · doi:10.1109/vcip49819.2020.9301857 · Cited by 8

Encoder-Decoder Structure With the Feature Pyramid for Depth Estimation From a Single Image

Tang, M., Chen, S., Dong, R., & Kan, J. (2021) · IEEE Access · doi:10.1109/access.2021.3055497 · Cited by 8

Unsupervised Learning of Depth and Depth-of-Field Effect from Natural Images with Aperture Rendering Generative Adversarial Networks

Kaneko, T. (2021) · 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/cvpr46437.2021.01542 · Cited by 8

Self-Supervised Monocular Scene Decomposition and Depth Estimation

Safadoust, S., & Güney, F. (2021) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv53792.2021.00072 · Cited by 8

Image-Based Texture Styling for Motion Effect Rendering

Lim, B., Han, S., & Choi, S. (2021) · ACM Symposium on Virtual Reality Software and Technology (VRST) · doi:10.1145/3489849.3489854 · Cited by 8

Single-image depth estimation using relative depths

Lee, J.-H., & Kim, C. (2022) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2022.103459 · Cited by 8

Do What You Can, With What You Have: Scale-aware and High Quality Monocular Depth Estimation Without Real World Labels

Swami, K., Muduli, A. K., Gurram, U., & Bajpai, P. (2022) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw56347.2022.00112 · Cited by 8

GTCaR: Graph Transformer for Camera Re-localization

Li, X., & Ling, H. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20080-9_14 · Cited by 8

3D-PL: Domain Adaptive Depth Estimation with 3D-Aware Pseudo-Labeling

Yen, Y.-T., Lu, C.-N., Chiu, W.-C., & Tsai, Y. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19812-0_41 · Cited by 8

FG-Depth: Flow-Guided Unsupervised Monocular Depth Estimation

Zhu, J., Liu, L., Liu, Y., Li, W., Wen, F., & Zhang, H. (2023) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48891.2023.10160534 · Cited by 8

Virtual Occlusions Through Implicit Depth

Watson, J., Sayed, M., Qureshi, Z., Brostow, G., Vicente, S., Aodha, O. M., & Firman, 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/cvpr52729.2023.00874 · Cited by 10

Robust Depth Estimation Using Auto-Exposure Bracketing

Im, S., Jeon, H., & Kweon, I. S. (2018) · IEEE Transactions on Image Processing · doi:10.1109/tip.2018.2886777 · Cited by 8

DVONet: Unsupervised Monocular Depth Estimation and Visual Odometry

Li, X., Hou, Y., Wu, Q., Wang, P., & Li, W. (2019) · IEEE International Conference on Visual Communications and Image Processing (VCIP) · doi:10.1109/vcip47243.2019.8965952 · Cited by 8

Semi-supervised image depth prediction with deep learning and binocular algorithms

Tseng, K.-K., Zhang, Y., Zhu, Q., Yung, K. L., & Ip, W. H. (2020) · Applied Soft Computing · doi:10.1016/j.asoc.2020.106272 · Cited by 9

Geometry understanding from autonomous driving scenarios based on feature refinement

Zhai, M., & Xiang, X. (2020) · Neural Computing and Applications · doi:10.1007/s00521-020-05192-z · Cited by 8

Superb Monocular Depth Estimation Based on Transfer Learning and Surface Normal Guidance

Huang, K., Qu, X., Chen, S., Chen, Z., Zhang, W., Qi, H., & Zhao, F. (2020) · Sensors · doi:10.3390/s20174856 · Cited by 8

A contextual conditional random field network for monocular depth estimation

Liu, J., Li, Q., Cao, R., Tang, W., & Qiu, G. (2020) · Image and Vision Computing · doi:10.1016/j.imavis.2020.103922 · Cited by 8

Dense Depth Posterior (DDP) from Single Image and Sparse RangePreprint

Yang, Y., Wong, A., & Soatto, S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1901.10034 · Cited by 8

Learning Scene Structure Guidance via Cross-Task Knowledge Transfer for Single Depth Super-ResolutionPreprint

Sun, B., Ye, X., Li, B., Li, H., Wang, Z., & Xu, R. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.12955 · Cited by 8

Depth estimation meets inverse rendering for single image novel view synthesis

Yu, Y., & Smith, W. A. P. (2019) · Conference on Visual Media Production · doi:10.1145/3359998.3369399 · Cited by 7

Self-Supervised Monocular Depth Estimation for All-Day Images Based on Dual-Axis Transformer

Hou, S., Fu, M., Wang, R., Yang, Y. M., & Song, W. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3406043 · Cited by 10

VFM-Depth: Leveraging Vision Foundation Model for Self-Supervised Monocular Depth Estimation

Yu, S., Wu, M., & Lam, S.-K. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3523702 · Cited by 9

Local plane estimation network with multi-scale fusion for efficient monocular depth estimation

Song, L., Jiang, B., & Song, H. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.128746 · Cited by 7

Real-time Monocular Dense Mapping for Augmented Reality

Xue, T., Luo, H., Cheng, D., Yuan, Z., & Yang, X. Y. (2017) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3123266.3123348 · Cited by 7

Spatiotemporally Enhanced Photometric Loss for Self-Supervised Monocular Depth Estimation

Zhang, T., Zhu, D., Zhang, G., Shi, W., Liu, Y., Zhang, X., & Li, J. (2022) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros47612.2022.9981595 · Cited by 7

Dimensions of Motion: Monocular Prediction through Flow Subspaces

Bowen, R. S., Tucker, R. P., Ramin Zabih, A., & Snavely, N. (2022) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv57658.2022.00057 · Cited by 7

RSTNet: Recurrent Spatial-Temporal Networks for Estimating Depth and Ego-Motion

Feng, T., & Gu, D. (2024) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2024.3360329 · Cited by 7

Learning Domain Invariant Features for Unsupervised Indoor Depth Estimation Adaptation

Zhang, J., Li, L., Yan, C. C., Wang, Z., Xu, C., Zhang, J., & Chen, C. (2024) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3672397 · Cited by 7

Dusk Till Dawn: Self-supervised Nighttime Stereo Depth Estimation using Visual Foundation Models

Vankadari, M. B., Hodgson, S., Shin, S., Zhou, K., Markham, A., & Trigoni, N. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10610318 · Cited by 9

On the Robustness of Language Guidance for Low-Level Vision Tasks: Findings from Depth Estimation

Chatterjee, A., Gokhale, T., Baral, C. R., & Yang, Y. (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/cvpr52733.2024.00270 · Cited by 7

Cross-Domain Synthetic-to-Real In-the-Wild Depth and Normal Estimation for 3D Scene Understanding

Bhanushali, J., Manivannan, M., & Chakravarthula, P. (2024) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00136 · Cited by 7

Embodiment: Self-Supervised Depth Estimation Based on Camera Models

Zhang, J., Reddy, P. K., Wong, X.-I., Aloimonos, Y., & Lu, G. (2024) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros58592.2024.10801343 · Cited by 8

Integrating convolutional guidance and Transformer fusion with Markov Random Fields smoothing for monocular depth estimation

Peng, X., Meng, Y., Shi, B., Zheng, C., & Wang, M. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.110011 · Cited by 7

NimbleD: Enhancing Self-supervised Monocular Depth Estimation with Pseudo-labels and Large-Scale Video Pre-training

Luginov, A., & Shahzad, M. S. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-92387-6_18 · Cited by 7

Improved appearance loss for deep estimation of image depth

El‐Shazly, E. H., Zhang, X., & Jiang, J. (2018) · Electronics Letters · doi:10.1049/el.2018.7656 · Cited by 7

Towards depth estimation in a single aerial image

Pellegrin, L., & Martínez-Carranza, J. (2019) · International Journal of Remote Sensing · doi:10.1080/01431161.2019.1681601 · Cited by 7

Unsupervised Learning of Camera Pose with Compositional Re-estimation

Nabavi, S. S., Hosseinzadeh, M., Fahimi, R., & Wang, Y. (2020) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv45572.2020.9093495 · Cited by 7

Transfer2Depth: Dual Attention Network With Transfer Learning for Monocular Depth Estimation

Yeh, C., Huang, Y.-P., Lin, C., & Chang, C. (2020) · IEEE Access · doi:10.1109/access.2020.2992815 · Cited by 8

Self-Supervised Attention Learning for Depth and Ego-motion Estimation

Sadek, A., & Chidlovskii, B. (2020) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros45743.2020.9340820 · Cited by 7

Self-supervised monocular depth estimation from oblique UAV videos

Madhuanand, L., Nex, F., & Yang, M. Y. (2021) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2021.03.024 · Cited by 64

Multi-Scale Residual Pyramid Attention Network for Monocular Depth Estimation

Liu, J., Zhang, X., Li, Z., & Mao, T. (2021) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr48806.2021.9412670 · Cited by 7

Self-supervised learning of monocular depth using quantized networks

Lu, K., Zeng, C., & Zeng, Y. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.11.071 · Cited by 7

Joint Disparity Estimation and Pseudo NIR Generation From Cross Spectral Image Pairs

Duan, Z., & Jung, C. (2022) · IEEE Access · doi:10.1109/access.2022.3140435 · Cited by 7

3D Reconstruction of Laparoscope Images With Contrastive Learning Methods

Chong, N. (2022) · IEEE Access · doi:10.1109/access.2022.3140334 · Cited by 7

High quality monocular depth estimation with parallel decoder

Liu, J., & Zhang, Y. (2022) · Scientific Reports · doi:10.1038/s41598-022-20909-x · Cited by 7

Unsupervised framework for depth estimation and camera motion prediction from video

Yang, D., Zhong, X., Gu, D., Peng, X., & Hu, H. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.12.049 · Cited by 17

Single View Depth Estimation via Dense Convolution Network with Self-supervision

Sun, Y., Shi, J., Bai, S., Qian, Q., & Sun, Z. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-37734-2_20

Unsupervised Multi-View Constrained Convolutional Network for Accurate Depth Estimation

Zhang, Y., Xu, S., Wu, B., Shi, J., Meng, W., & Zhang, X. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2020.2997247 · Cited by 26

An Adaptive Unsupervised Learning Framework for Monocular Depth Estimation

Yang, D., Zhong, X., Lin, L., & Peng, X. (2019) · IEEE Access · doi:10.1109/access.2019.2946323 · Cited by 4

AsiANet: Autoencoders in Autoencoder for Unsupervised Monocular Depth Estimation

Yusiong, J. P. T., & Naval, P. C. (2019) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2019.00053 · Cited by 8