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

Real-time Monocular Depth Estimation with Sparse Supervision on Mobile

Yucel, M. K., Dimaridou, V., Drosou, A., & Saà-Garriga, A. (2021) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw53098.2021.00275 · Cited by 27

Enabling Energy-Efficient Unsupervised Monocular Depth Estimation on ARMv7-Based Platforms

Peluso, V., Cipolletta, A., Calimera, A., Poggi, M., Tosi, F., & Mattoccia, S. (2019) · Design, Automation, and Test in Europe · doi:10.23919/date.2019.8714893 · Cited by 27

Data-Efficient Learning for Sim-to-Real Robotic Grasping using Deep Point Cloud Prediction NetworksPreprint

Yan, X., Khansari, M., Hsu, J., Gong, Y., Bai, Y., Pirk, S., & Lee, H. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.08989 · Cited by 25

Multimodal Scale Consistency and Awareness for Monocular Self-Supervised Depth Estimation

Chawla, H., Varma, A., Arani, E., & Zonooz, B. (2021) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48506.2021.9561441 · Cited by 26

Self-supervised multi-frame depth estimation with visual-inertial pose transformer and monocular guidance

Wang, X., Luo, H., Wang, Z., Zheng, J. Y., & Bai, X. P. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102363 · Cited by 24

TinyDepth: Lightweight self-supervised monocular depth estimation based on transformer

Cheng, Z., Zhang, Y., Yu, Y., Song, Z., & Tang, C. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.109313 · Cited by 33

Depth Estimation of Video Sequences With Perceptual Losses

Wang, A., Fang, Z., Gao, Y., Jiang, X., & Ma, S. (2018) · IEEE Access · doi:10.1109/access.2018.2846546 · Cited by 24

Structured Coupled Generative Adversarial Networks for Unsupervised Monocular Depth Estimation

Puscas, M., Xu, D., Pilzer, A., & Sebe, N. (2019) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2019.00012 · Cited by 24

Wavelet Synthesis Net for Disparity Estimation to Synthesize DSLR Calibre Bokeh Effect on Smartphones

Luo, C.-C., Li, Y., Lin, K., Chen, G., Lee, S.-J., Choi, J., Yoo, Y., & Polley, M. O. (2020) · Computer Vision and Pattern Recognition · doi:10.1109/cvpr42600.2020.00248 · Cited by 22

Depth estimation from a single SEM image using pixel-wise fine-tuning with multimodal data

Houben, T., Huisman, T. J., Pisarenco, M., van der Sommen, F., & de With, P. H. N. (2022) · Machine Vision and Applications · doi:10.1007/s00138-022-01314-w · Cited by 26

Edge Devices Friendly Self-Supervised Monocular Depth Estimation via Knowledge Distillation

Gao, W., Rao, D. R., Yang, Y., & Chen, J. (2023) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2023.3330054 · Cited by 27

WeatherDepth: Curriculum Contrastive Learning for Self-Supervised Depth Estimation under Adverse Weather Conditions

Wang, J., Lin, C., Nie, L., Huang, S., Zhao, Y., Pan, X., & Ai, R. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10611100 · Cited by 33

SharinGAN: Combining Synthetic and Real Data for Unsupervised Geometry Estimation

Koutilya, P. N. V. R., Zhou, H., & Jacobs, D. W. (2020) · 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/cvpr42600.2020.01399 · Cited by 42

Self-Supervised Joint Learning Framework of Depth Estimation via Implicit CuesPreprint

Wang, J., Zhang, G., Wu, Z., Li, X., & Liu, L. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.09876 · Cited by 24

MonoProb: Self-Supervised Monocular Depth Estimation with Interpretable Uncertainty

Marsal, R., Chabot, F., Loesch, A., Grolleau, W., & Sahbi, H. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00360 · Cited by 25

Diffusion Models for Monocular Depth Estimation: Overcoming Challenging Conditions

Tosi, F., Ramirez, P. Z., & Poggi, M. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73337-6_14 · Cited by 34

Meaningful maps with object-oriented semantic mapping

Sünderhauf, N., Pham, T. T., Latif, Y., Milford, M. J., & Reid, I. D. (2017) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2017.8206392 · Cited by 23

Dense monocular reconstruction using surface normals

Weerasekera, C. S., Latif, Y., Garg, R., & Reid, I. D. (2017) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2017.7989293 · Cited by 23

Learning Pose Specific Representations by Predicting Different Views

Poier, G., Schinagl, D., & Bischof, H. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00014 · Cited by 22

AUTO3D: Novel View Synthesis Through Unsupervisely Learned Variational Viewpoint and Global 3D Representation

Liu, X., Che, T., Lu, Y., Yang, C., Li, S., & You, J. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58545-7_4 · Cited by 23

3D plant root system reconstruction based on fusion of deep structure-from-motion and IMU

Lu, Y., Wang, Y., Chen, Z., Khan, A., Salvaggio, C., & Lu, G. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-020-10069-3 · Cited by 24

DiPE: Deeper into Photometric Errors for Unsupervised Learning of Depth and Ego-motion from Monocular Videos

Jiang, H., Ding, L., Sun, Z., & Huang, R. (2020) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros45743.2020.9341074 · Cited by 25

H-Net: Unsupervised Attention-based Stereo Depth Estimation Leveraging Epipolar Geometry

Huang, B., Zheng, J.-Q., Giannarou, S., & Elson, D. S. (2022) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw56347.2022.00492 · Cited by 27

Underwater self-supervised depth estimation

Yang, X., Zhang, X., Wang, N., Xin, G., & Hu, W. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.09.122 · Cited by 23

Smart lighting control system based on fusion of monocular depth estimation and multi-object detection

Shen, D., Ning, C., Wang, Y., Duan, W., & Duan, P. (2022) · Energy and Buildings · doi:10.1016/j.enbuild.2022.112485 · Cited by 23

Adaptive Co-teaching for Unsupervised Monocular Depth Estimation

Ren, W., Wang, L., Piao, Y., Zhang, M., Lu, H., & Liu, T. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19769-7_6 · Cited by 25

Self-Supervised Depth Estimation Leveraging Global Perception and Geometric Smoothness

Jia, S., Pei, X., Yao, W., & Wong, S. C. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3219604 · Cited by 27

Trap Attention: Monocular Depth Estimation with Manual Traps

Ning, C., & Gan, H. (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/cvpr52729.2023.00487 · Cited by 25

A Multi-Task Vision Transformer for Segmentation and Monocular Depth Estimation for Autonomous Vehicles

Bavirisetti, D. P., Martinsen, H. R., Kiss, G. H., & Lindseth, F. (2023) · IEEE Open Journal of Intelligent Transportation Systems · doi:10.1109/ojits.2023.3335648 · Cited by 28

CReaM: Condensed Real-time Models for Depth Prediction using Convolutional Neural Networks

Spek, A., Dharmasiri, T., & Drummond, T. W. (2018) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2018.8594243 · Cited by 24

Unsupervised Learning-Based Depth Estimation-Aided Visual SLAM Approach

Geng, M., Shang, S., Ding, B., Wang, H., & Zhang, P. (2019) · Circuits Systems and Signal Processing · doi:10.1007/s00034-019-01173-3 · Cited by 23

Footprints and Free Space From a Single Color Image

Watson, J., Firman, M., Monszpart, Á., & Brostow, G. (2020) · 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/cvpr42600.2020.00009 · Cited by 23

Semiparallel deep neural network hybrid architecture: first application on depth from monocular camera

Bazrafkan, S., Javidnia, H., Lemley, J., & Corcoran, P. (2018) · Journal of Electronic Imaging · doi:10.1117/1.jei.27.4.043041 · Cited by 23

Probabilistic Graph Attention Network with Conditional Kernels for Pixel-Wise Prediction

Xu, D., Alameda-Pineda, X., Ouyang, W., Ricci, E., Wang, X., & Sebe, N. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.3043781 · Cited by 24

SharinGAN: Combining Synthetic and Real Data for Unsupervised Geometry EstimationPreprint

Pnvr, K., Zhou, H., & Jacobs, D. W. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.04026 · Cited by 23

Learning 3D Dynamic Scene Representations for Robot ManipulationPreprint

Xu, Z., He, Z., Wu, J., & Song, S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.01968 · Cited by 23

Advances in Real-Time 3D Reconstruction for Medical Endoscopy

Richter, A., Steinmann, T., Rosenthal, J.-C., & Rupitsch, S. J. (2024) · Journal of Imaging · doi:10.3390/jimaging10050120 · Cited by 26

Deep learning-based 3D reconstruction from multiple images: A survey

Wang, C., Reza, M. A., Vats, V., Ju, Y., Thakurdesai, N., Wang, Y., Crandall, D., Jung, S., & Seo, J. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.128018 · Cited by 23

SPARE3D: A Dataset for SPAtial REasoning on Three-View Line Drawings

Han, W., Xiang, S., Liu, C., Wang, R., & Feng, C. (2020) · 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/cvpr42600.2020.01470 · Cited by 25

PLNet: Plane and Line Priors for Unsupervised Indoor Depth Estimation

Jiang, H., Ding, L., Hu, J., & Huang, R. (2021) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv53792.2021.00083 · Cited by 21

Self-supervised monocular depth estimation in dynamic scenes with moving instance loss

Yue, M., Fu, G., Wu, M., Zhang, X., & Gu, H. (2022) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2022.104862 · Cited by 22

3D map-guided single indoor image localization refinement

Li, Q., Zhu, J., Liu, J., Cao, R., Fu, H., Garibaldi, J. M., Li, Q., Liu, B., & Qiu, G. (2020) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2020.01.008 · Cited by 22

$$S^3$$Net: Semantic-Aware Self-supervised Depth Estimation with Monocular Videos and Synthetic Data

Cheng, B., Saggu, I. S., Shah, R., Bansal, G., & Bharadia, D. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58577-8_4 · Cited by 24

A Survey on Deep Learning Architectures for Image-based Depth ReconstructionPreprint

Laga, H. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.06113 · Cited by 22

SUW-Learn: Joint Supervised, Unsupervised, Weakly Supervised Deep Learning for Monocular Depth Estimation

Ren, H., Raj, A., El‐Khamy, M., & Lee, J. (2020) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw50498.2020.00383 · Cited by 21

Single-Image Depth Prediction Makes Feature Matching Easier

Toft, C., Turmukhambetov, D., Sattler, T., Kahl, F., & Brostow, G. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58517-4_28 · Cited by 24

Constant Velocity Constraints for Self-Supervised Monocular Depth Estimation

Zhou, H., Greenwood, D., Taylor, S. L., & Gong, H. (2020) · Conference on Visual Media Production · doi:10.1145/3429341.3429355 · Cited by 22

Self-Supervised 3D Reconstruction and Ego-Motion Estimation Via On-Board Monocular Video

Jia, S., Pei, X., Jing, X., & Yao, D. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3071428 · Cited by 21

Texture-Generic Deep Shape-From-Template

Fuentes-Jimenez, D., Pizarro, D., Casillas-Pérez, D., Collins, T., & Bartoli, A. (2021) · IEEE Access · doi:10.1109/access.2021.3082011 · Cited by 21

Plugging Self-Supervised Monocular Depth into Unsupervised Domain Adaptation for Semantic Segmentation

Cardace, A., Luigi, L. D., Ramirez, P. Z., Salti, S., & Stefano, L. D. (2022) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv51458.2022.00206 · Cited by 22

Monocular Depth Estimation with Self-Supervised Learning for Vineyard Unmanned Agricultural Vehicle

Cui, X.-Z., Feng, Q., Wang, S.-Z., & Zhang, J. (2022) · Sensors · doi:10.3390/s22030721 · Cited by 22

Gated2Gated: Self-Supervised Depth Estimation from Gated Images

Walia, A., Walz, S., Bijelic, M., Mannan, F., Julca-Aguilar, F., Langer, M., Ritter, W., & Heide, F. (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/cvpr52688.2022.00283 · Cited by 21

Learning Occlusion-aware Coarse-to-Fine Depth Map for Self-supervised Monocular Depth Estimation

Zhou, Z., & Dong, Q. (2022) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3503161.3548381 · Cited by 23

Self-Supervised Monocular Depth Estimation With Geometric Prior and Pixel-Level Sensitivity

Liu, J., Cao, Z., Liu, X., Wang, S., & Yu, J. (2022) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2022.3210274 · Cited by 21

GlocalFuse-Depth: Fusing transformers and CNNs for all-day self-supervised monocular depth estimation

Zhang, Z., Chan, R. K. Y., & Wong, K. K. Y. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.127122 · Cited by 22

Mono-ViFI: A Unified Learning Framework for Self-supervised Single and Multi-frame Monocular Depth Estimation

Liu, J., Kong, L., Li, B., Wang, Z., Gu, H., & Chen, J. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72995-9_6 · Cited by 23

Learning Residual Flow as Dynamic Motion from Stereo Videos

Lee, S., Im, S., Lin, S. C.-F., & Kweon, I. S. (2019) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros40897.2019.8967970 · Cited by 21

Monocular Depth Estimation in New Environments With Absolute Scale

Roussel, T., Eycken, L. V., & Tuytelaars, T. (2019) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros40897.2019.8967677 · Cited by 21

Windowed Bundle Adjustment Framework for Unsupervised Learning of Monocular Depth Estimation With U-Net Extension and Clip Loss

Zhou, L., & Kaess, M. (2020) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2020.2976301 · Cited by 21

Neural Ray Surfaces for Self-Supervised Learning of Depth and Ego-motion

Vasiljevic, I., Guizilini, V., Ambruş, R., Pillai, S., Burgard, W., Shakhnarovich, G., & Gaidon, A. (2020) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv50981.2020.00010 · Cited by 21

Towards unified on-road object detection and depth estimation from a single image

Lian, G., Wang, Y., Qin, H., & Chen, G. (2021) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-021-01444-z · Cited by 21

Learning to refine depth for robust stereo estimation

Cheng, F., He, X., & Zhang, H. (2017) · Pattern Recognition · doi:10.1016/j.patcog.2017.07.027 · Cited by 21

ALIGNet: Partial-Shape Agnostic Alignment via Unsupervised LearningPreprint

Hanocka, R., Fish, N., Wang, Z., Giryes, R., Fleishman, S., & Cohen‐Or, D. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1804.08497 · Cited by 21

Targeted Adversarial Perturbations for Monocular Depth PredictionPreprint

Wong, A., Cicek, S., & Soatto, S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.08602 · Cited by 22

Review of monocular depth estimation methods

Zhang, Z., Zhang, Y., Li, Y., & Wu, L. (2025) · Journal of Electronic Imaging · doi:10.1117/1.jei.34.2.020901 · Cited by 23

Monocular Visual Object 3D Localization in Road Scenes

Wang, Y., Huang, Y.-T., & Hwang, J. (2019) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3343031.3350924 · Cited by 20

Self-supervised Depth Estimation to Regularise Semantic Segmentation in Knee Arthroscopy

Liu, F., Jonmohamadi, Y., Maicas, G., Pandey, A. K., & Carneiro, G. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59710-8_58 · Cited by 20

Self-Supervised Learning of Domain Invariant Features for Depth Estimation

Akada, H., Bhat, S. F., Alhashim, I., & Wonka, P. (2022) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv51458.2022.00107 · Cited by 20

Self-Supervised Single-Image Depth Estimation From Focus and Defocus Clues

Lu, Y., Milliron, G. W., Slagter, J., & Lu, G. (2021) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2021.3092258 · Cited by 21

Deep Unsupervised Learning Based Visual Odometry with Multi-scale Matching and Latent Feature Constraint

Liang, Z., Wang, Q., & Yu, Y. (2021) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros51168.2021.9636131 · Cited by 20

ChiTransformer: Towards Reliable Stereo from Cues

Su, Q. F., & Ji, S. (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/cvpr52688.2022.00198 · Cited by 21

Self-Supervised Monocular Depth Estimation With 3-D Displacement Module for Laparoscopic Images

Xu, C., Huang, B., & Elson, D. S. (2022) · IEEE Transactions on Medical Robotics and Bionics · doi:10.1109/tmrb.2022.3170206 · Cited by 20

Multi-Camera Collaborative Depth Prediction via Consistent Structure Estimation

Xu, J., Liu, X., Bai, Y., Jiang, J., Wang, K., Chen, X., & Ji, X. (2022) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3503161.3548394 · Cited by 20

Fusion of Stereo and Still Monocular Depth Estimates in a Self-Supervised Learning Context

Martins, D. R., van Hecke, K. G., & de Croon, G. (2018) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2018.8461116 · Cited by 20

A two-streamed network for estimating fine-scaled depth maps from single RGB images

Li, J., Yuce, C., Klein, R., & Yao, A. (2019) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2019.06.002 · Cited by 20

DurLAR: A High-Fidelity 128-Channel LiDAR Dataset with Panoramic Ambient and Reflectivity Imagery for Multi-Modal Autonomous Driving Applications

Li, L., Ismail, K. N., Shum, H. P. H., & Breckon, T. P. (2021) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv53792.2021.00130 · Cited by 21

Unsupervised Learning of Monocular Depth and Ego-Motion in Outdoor/Indoor Environments

Gao, R., Xiao, X., Xing, W., Li, C., & Liu, L. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3151629 · Cited by 21

World From Blur

Qiu, J., Wang, X., Maybank, S. J., & Tao, D. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00869 · Cited by 20

Monocular depth estimation using self-supervised learning with more effective geometric constraints

Xiong, M., Zhang, Z., Liu, J., Zhang, T., & Xiong, H. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.107489 · Cited by 19

The Third Monocular Depth Estimation Challenge

Spencer, J., Tosi, F., Poggi, M., Arora, R. S., Russell, C., Hadfield, S., Bowden, R., Zhou, G., Li, Z., Rao, Q., et al. (2024) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00005 · Cited by 21

Depth estimation from single monocular images using deep hybrid network

Grigorev, A., Jiang, F., Rho, S., Jifara, W., Liu, S., & Sai, S. (2016) · Multimedia Tools and Applications · doi:10.1007/s11042-016-4200-x · Cited by 19

Monocular Depth Estimation by Learning from Heterogeneous Datasets

Gurram, A., Urfalıoǧlu, O., Halfaoui, I., Bouzaraa, F., & López, A. M. (2018) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2018.8500683 · Cited by 19

Monocular Depth Estimation Using Synthetic Images With Shadow Removal

Guo, R., Ayinde, B. O., Sun, H., Muralidharan, H., & Oguchi, K. (2019) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2019.8916914 · Cited by 19

FlowReg: Fast Deformable Unsupervised Medical Image Registration using Optical Flow

Mocanu, S., Moody, A. R., & Khademi, A. (2021) · The Journal of Machine Learning for Biomedical Imaging · doi:10.59275/j.melba.2021-3581 · Cited by 19

Unsupervised Learning of Monocular Depth Estimation with Bundle Adjustment, Super-Resolution and Clip LossPreprint

Zhou, L., Ye, J., Abello, M., Wang, S., & Kaess, M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1812.03368 · Cited by 23

Monocular Depth Estimation with Self-supervised Instance AdaptationPreprint

McCraith, R., Neumann, L., Zisserman, A., & Vedaldi, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.05821 · Cited by 26

Attention based multilayer feature fusion convolutional neural network for unsupervised monocular depth estimation

Lei, Z., Wang, Y., Li, Z., & Yang, J. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.11.002 · Cited by 17

Deep Cross Spectral Stereo Matching Using Multi-Spectral Image Fusion

Liang, X., & Jung, C. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3155202 · Cited by 18

Deep learning of monocular depth, optical flow and ego-motion with geometric guidance for UAV navigation in dynamic environments

Mumuni, F., Mumuni, A., & Amuzuvi, C. K. (2022) · Machine Learning with Applications · doi:10.1016/j.mlwa.2022.100416 · Cited by 20

CroMo: Cross-Modal Learning for Monocular Depth Estimation

Verdié, Y., Song, J., Mas, B., Busam, B., Leonardis, A., & McDonagh, S. (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/cvpr52688.2022.00391 · Cited by 19

LAM-Depth: Laplace-Attention Module-Based Self-Supervised Monocular Depth Estimation

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UseGeo - A UAV-based multi-sensor dataset for geospatial research

Nex, F., Stathopoulou, E. K., Fabio Remondino, A., Yang, M. Y., Madhuanand, L., Yogender, Y., Alsadik, B. S. A., Weinmann, M., Jutzi, B., & Qin, R. (2024) · ISPRS Open Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.ophoto.2024.100070 · Cited by 26

Depth Estimation From a Single Image Using Guided Deep Network

Song, M., & Kim, W. (2019) · IEEE Access · doi:10.1109/access.2019.2944937 · Cited by 18

Self-Supervised Learning for Autonomous Vehicles Perception: A Conciliation Between Analytical and Learning Methods

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Accurate, Low-Latency Visual Perception for Autonomous Racing: Challenges, Mechanisms, and Practical Solutions

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On Incremental Structure from Motion Using Lines

Mateus, A., Tahri, O., Aguiar, A. P., Lima, P. U., & Miraldo, P. (2021) · IEEE Transactions on Robotics · doi:10.1109/tro.2021.3085487 · Cited by 18

PDANet: Self-Supervised Monocular Depth Estimation Using Perceptual and Data Augmentation Consistency

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Hierarchical Object Relationship Constrained Monocular Depth Estimation.

Li, S., Shi, J., Song, W., Hao, A., & Qin, H. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2021.108116 · Cited by 18

Sparse2Dense: From Direct Sparse Odometry to Dense 3-D Reconstruction

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Unsupervised colonoscopic depth estimation by domain translations with a Lambertian-reflection keeping auxiliary task

Itoh, H., Oda, M., Mori, Y., Misawa, M., Kudo, S., Imai, K., Ito, S., Hotta, K., Takabatake, H., Mori, M., et al. (2021) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-021-02398-x · Cited by 18

Unsupervised framework for depth estimation and camera motion prediction from video

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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

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An Adaptive Unsupervised Learning Framework for Monocular Depth Estimation

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AsiANet: Autoencoders in Autoencoder for Unsupervised Monocular Depth Estimation

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