Depth and stereo: research map
1,365 accepted papers on Depth and stereo in 3D vision, from ICML, NeurIPS, ICLR, CVPR and AAAI (2016–2026), grouped into 4 clusters and 16 approaches. The busiest year so far is 2026.
Within 3D vision, its share shrank from 17.2% in 2023–24 to 14.8% in 2025–26 (306 → 409 papers at ICML, NeurIPS, CVPR and AAAI, the venues with data for all four years).
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Approaches and key papers
motion · flow · localization · 532 papers
Approaches in this cluster:
- Feed-forward 3D reconstruction (133 papers)
Reconstructs scenes and structure-from-motion in a single pass with learned priors, planes and dense matching. - Camera model and calibration for SfM (99 papers)
Handles camera calibration, distortion and minimal geometric solvers in large-scale structure from motion. - Learned optical and scene flow (108 papers)
Estimates motion fields with unrolled, unsupervised or joint learning of flow, depth and ego-motion. - Scene coordinate regression for localization (112 papers)
Localizes cameras by regressing scene coordinates or maps, with voting and 3D surface prediction. - Dense feature matching for correspondence (80 papers)
Learns dense or two-view correspondences with kernelized matching, cycle consistency and confidence estimation.
Most cited and most cited since 2024:
- Structure-From-Motion Revisited (CVPR 2016 · 6,291 citations)
- SuperGlue: Learning Feature Matching With Graph Neural Networks (CVPR 2020 · 2,845 citations)
- DUSt3R: Geometric 3D Vision Made Easy (CVPR 2024 · 545 citations)
- SplaTAM: Splat Track & Map 3D Gaussians for Dense RGB-D SLAM (CVPR 2024 · 438 citations)
depth estimation · monocular · depth map · 342 papers
Approaches in this cluster:
- Self-supervised monocular depth (116 papers)
Learns depth from unlabeled images or video using geometric constraints, semantic guidance and planarity priors. - Dense depth prediction and completion (85 papers)
Predicts consistent, high-resolution or sparse-input-completed depth from single images and video. - Foundation models for monocular geometry (81 papers)
Estimates geometry from single images with inductive biases, open-domain training and synthetic-real adaptation. - Depth from light field and defocus (60 papers)
Estimates depth using light fields, defocus, event cues and interference instead of standard RGB.
Most cited and most cited since 2024:
- Unsupervised Monocular Depth Estimation With Left-Right Consistency (CVPR 2017 · 3,325 citations)
- Unsupervised Learning of Depth and Ego-Motion From Video (CVPR 2017 · 2,885 citations)
- VGGT: Visual Geometry Grounded Transformer (CVPR 2025 · 410 citations)
- Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation (CVPR 2024 · 388 citations)
light · imaging · illumination · 288 papers
Approaches in this cluster:
- Neural light field and illumination imaging (107 papers)
Recovers scene properties with learned light fields, structured light and material estimation. - Non-line-of-sight imaging (93 papers)
Reconstructs hidden or transparent geometry from time-of-flight light transport. - Spike and event camera reconstruction (50 papers)
Reconstructs high-speed scenes from spike, single-photon and neuromorphic sensor data. - Snapshot hyperspectral imaging (38 papers)
Reconstructs spectral images from coded or dispersive snapshots with learned or physics-guided models.
Most cited and most cited since 2024:
- DeepView: View Synthesis With Learned Gradient Descent (CVPR 2019 · 434 citations)
- EPINET: A Fully-Convolutional Neural Network Using Epipolar Geometry for Depth From Light Field Images (CVPR 2018 · 317 citations)
- Boosting Spike Camera Image Reconstruction from a Perspective of Dealing with Spike Fluctuations (CVPR 2024 · 16 citations)
- Progressive Divide-and-Conquer via Subsampling Decomposition for Accelerated MRI (CVPR 2024 · 16 citations)
view stereo · photometric stereo · stereo matching · 203 papers
Approaches in this cluster:
- Cost-volume multi-view stereo (107 papers)
Infers depth from multiple views with learned cost volumes, recurrent regularization and geometry-aware networks. - Photometric stereo (43 papers)
Estimates surface normals from varying illumination, including uncalibrated and non-Lambertian cases. - Learned stereo with specialized sensors (53 papers)
Estimates depth from stereo pairs with active, polarization, normal or exposure cues.
Most cited and most cited since 2024:
- Pyramid Stereo Matching Network (CVPR 2018 · 1,860 citations)
- A Multi-View Stereo Benchmark With High-Resolution Images and Multi-Camera Videos (CVPR 2017 · 913 citations)
- MoCha-Stereo: Motif Channel Attention Network for Stereo Matching (CVPR 2024 · 86 citations)
- MonSter: Marry Monodepth to Stereo Unleashes Power (CVPR 2025 · 53 citations)
Related topics in 3D vision
- Gaussian splatting (841)
- Human pose estimation (1,051)
- NeRF and radiance fields (456)
- 3D generation (1,606)
- Face and shape (1,286)