Data to 14 Oct 2026

GeoNet: Unsupervised Learning of Dense Depth, Optical Flow and Camera Pose

arXiv 1803.02276 ↗First posted 6 Mar 2018cs.CVcited by 342

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Abstract (arXiv)

We propose GeoNet, a jointly unsupervised learning framework for monocular depth, optical flow and ego-motion estimation from videos. The three components are coupled by the nature of 3D scene geometry, jointly learned by our framework in an end-to-end manner. Specifically, geometric relationships are extracted over the predictions of individual modules and then combined as an image reconstruction loss, reasoning about static and dynamic scene parts separately. Furthermore, we propose an adaptive geometric consistency loss to increase robustness towards outliers and non-Lambertian regions, which resolves occlusions and texture ambiguities effectively. Experimentation on the KITTI driving dataset reveals that our scheme achieves state-of-the-art results in all of the three tasks, performing better than previously unsupervised methods and comparably with supervised ones.

Key facts. Cited by 342 papers in the map (data through 14 Oct 2026). First posted on arXiv 6 Mar 2018.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 90; 2022: 91; 2023: 73; 2024: 42; 2025: 30; 2026: 16.

342 papers.

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2023DUSt3R: Geometric 3D Vision Made Easycited by 1,567
2021BARF: Bundle-Adjusting Neural Radiance Fieldscited by 642
2024Metric3Dv2: A Versatile Monocular Geometric Foundation Model for Zero-shot Metric Depth and Surface Normal Estimationcited by 395
2023Metric3D: Towards Zero-shot Metric 3D Prediction from A Single Imagecited by 365
2021Omnidata: A Scalable Pipeline for Making Multi-Task Mid-Level Vision Datasets from 3D Scanscited by 336
2023SurroundOcc: Multi-Camera 3D Occupancy Prediction for Autonomous Drivingcited by 316
2022Unifying Flow, Stereo and Depth Estimationcited by 275
2023A Survey on Self-supervised Learning: Algorithms, Applications, and Future Trendscited by 207
2023Robust Dynamic Radiance Fieldscited by 194
2021Probabilistic and Geometric Depth: Detecting Objects in Perspectivecited by 191
2021The Temporal Opportunist: Self-Supervised Multi-Frame Monocular Depthcited by 190
2024Splatt3R: Zero-shot Gaussian Splatting from Uncalibrated Image Pairscited by 178

38 more

2022ASpanFormer: Detector-Free Image Matching with Adaptive Span Transformercited by 171
2022MonoViT: Self-Supervised Monocular Depth Estimation with a Vision Transformercited by 136
2022P3Depth: Monocular Depth Estimation with a Piecewise Planarity Priorcited by 107
2023Progressively Optimized Local Radiance Fields for Robust View Synthesiscited by 103
2022RelPose: Predicting Probabilistic Relative Rotation for Single Objects in the Wildcited by 102
2022Lite-Mono: A Lightweight CNN and Transformer Architecture for Self-Supervised Monocular Depth Estimationcited by 100
2022ParticleSfM: Exploiting Dense Point Trajectories for Localizing Moving Cameras in the Wildcited by 92
2022SurroundDepth: Entangling Surrounding Views for Self-Supervised Multi-Camera Depth Estimationcited by 76
2021SMURF: Self-Teaching Multi-Frame Unsupervised RAFT with Full-Image Warpingcited by 74
2021Self-Supervised Monocular Depth and Ego-Motion Estimation in Endoscopy: Appearance Flow to the Rescuecited by 72
2024Deep Patch Visual SLAMcited by 71
2021Unsupervised Scale-consistent Depth Learning from Videocited by 69
2022Multi-Frame Self-Supervised Depth with Transformerscited by 64
2021Channel-Wise Attention-Based Network for Self-Supervised Monocular Depth Estimationcited by 63
2021Learning Monocular Depth in Dynamic Scenes via Instance-Aware Projection Consistencycited by 58
2021Regularizing Nighttime Weirdness: Efficient Self-supervised Monocular Depth Estimation in the Darkcited by 58
2021OmniDet: Surround View Cameras based Multi-task Visual Perception Network for Autonomous Drivingcited by 57
2021Consistent Depth of Moving Objects in Videocited by 56
2024LEAP-VO: Long-term Effective Any Point Tracking for Visual Odometrycited by 56
2022TransCG: A Large-Scale Real-World Dataset for Transparent Object Depth Completion and a Grasping Baselinecited by 55
2022Robust Cross-Modal Representation Learning with Progressive Self-Distillationcited by 55
2022SC-DepthV3: Robust Self-supervised Monocular Depth Estimation for Dynamic Scenescited by 52
2021Wide-Baseline Relative Camera Pose Estimation with Directional Learningcited by 49
2021Excavating the Potential Capacity of Self-Supervised Monocular Depth Estimationcited by 48
2022Disentangling Object Motion and Occlusion for Unsupervised Multi-frame Monocular Depthcited by 48
2024Stereo Anywhere: Robust Zero-Shot Deep Stereo Matching Even Where Either Stereo or Mono Failcited by 47
2021UnsupervisedR&R: Unsupervised Point Cloud Registration via Differentiable Renderingcited by 44
2022Towards Accurate Reconstruction of 3D Scene Shape from A Single Monocular Imagecited by 42
2024SelfSplat: Pose-Free and 3D Prior-Free Generalizable 3D Gaussian Splattingcited by 42
2022textttGradICON: Approximate Diffeomorphisms via Gradient Inverse Consistencycited by 41
2023OccNeRF: Advancing 3D Occupancy Prediction in LiDAR-Free Environmentscited by 41
2021Sparse Auxiliary Networks for Unified Monocular Depth Prediction and Completioncited by 39
2021Deep Two-View Structure-from-Motion Revisitedcited by 39
2021Self-Point-Flow: Self-Supervised Scene Flow Estimation from Point Clouds with Optimal Transport and Random Walkcited by 39
2021TANDEM: Tracking and Dense Mapping in Real-time using Deep Multi-view Stereocited by 39
2023FlowCam: Training Generalizable 3D Radiance Fields without Camera Poses via Pixel-Aligned Scene Flowcited by 39
2023R3D3: Dense 3D Reconstruction of Dynamic Scenes from Multiple Camerascited by 39
2021MonoIndoor: Towards Good Practice of Self-Supervised Monocular Depth Estimation for Indoor Environmentscited by 38

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