mlsanityData to 7 Oct 2026

Deep Learning for Multi-View Stereo via Plane Sweep: A Survey

arXiv 2106.15328 ↗First posted 18 Jun 2021cs.CVcited by 16

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Abstract (arXiv)

3D reconstruction has lately attracted increasing attention due to its wide application in many areas, such as autonomous driving, robotics and virtual reality. As a dominant technique in artificial intelligence, deep learning has been successfully adopted to solve various computer vision problems. However, deep learning for 3D reconstruction is still at its infancy due to its unique challenges and varying pipelines. To stimulate future research, this paper presents a review of recent progress in deep learning methods for Multi-view Stereo (MVS), which is considered as a crucial task of image-based 3D reconstruction. It also presents comparative results on several publicly available datasets, with insightful observations and inspiring future research directions.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). Builds on 18 of its 30 references that are in the map. First posted on arXiv 18 Jun 2021.

Builds on

18 of 30 references.

References · 18

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  3. 2015FlowNet: Learning Optical Flow with Convolutional Networkscited by 1,054
  4. 2015A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimationcited by 878
  5. 2018MVSNet: Depth Inference for Unstructured Multi-view Stereocited by 780
  6. 2018Free-Form Image Inpainting with Gated Convolutioncited by 594
  7. 2019BlendedMVS: A Large-scale Dataset for Generalized Multi-view Stereo Networkscited by 487
  8. 2019Cascade Cost Volume for High-Resolution Multi-View Stereo and Stereo Matchingcited by 414
  9. 2017End-to-End Learning of Geometry and Context for Deep Stereo Regressioncited by 359
  10. 2019Recurrent MVSNet for High-resolution Multi-view Stereo Depth Inferencecited by 252
  11. 2019Cost Volume Pyramid Based Depth Inference for Multi-View Stereocited by 154
  12. 2019Deep Stereo using Adaptive Thin Volume Representation with Uncertainty Awarenesscited by 151
6 more
  1. 2019Point-Based Multi-View Stereo Networkcited by 147
  2. 2019DPSNet: End-to-end Deep Plane Sweep Stereocited by 115
  3. 2020Visibility-aware Multi-view Stereo Networkcited by 97
  4. 2020Dense Hybrid Recurrent Multi-view Stereo Net with Dynamic Consistency Checkingcited by 81
  5. 2019Normal Assisted Stereo Depth Estimationcited by 49
  6. 2019Pyramid Multi-view Stereo Net with Self-adaptive View Aggregationcited by 44

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 4; 2024: 9; 2025: 1; 2026: 1.

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  1. 2024Is Sora a World Simulator? A Comprehensive Survey on General World Models and Beyondcited by 111
  2. 2022BEVStereo: Enhancing Depth Estimation in Multi-view 3D Object Detection with Dynamic Temporal Stereocited by 89
  3. 2024DriveWorld: 4D Pre-trained Scene Understanding via World Models for Autonomous Drivingcited by 89
  4. 2023UniWorld: Autonomous Driving Pre-training via World Modelscited by 32
  5. 2023UniScene: Multi-Camera Unified Pre-training via 3D Scene Reconstruction for Autonomous Drivingcited by 15
  6. 2024Geometry Field Splatting with Gaussian Surfelscited by 13
  7. 2024FoVA-Depth: Field-of-View Agnostic Depth Estimation for Cross-Dataset Generalizationcited by 8
  8. 2024Autonomous Driving in Unstructured Environments: How Far Have We Come?cited by 7
  9. 2023LiveNVS: Neural View Synthesis on Live RGB-D Streamscited by 6
  10. 2023BEVStereo++: Accurate Depth Estimation in Multi-view 3D Object Detection via Dynamic Temporal Stereocited by 4
  11. 2024Toward Accurate Camera-based 3D Object Detection via Cascade Depth Estimation and Calibrationcited by 3
  12. 2024A Global Depth-Range-Free Multi-View Stereo Transformer Network with Pose Embeddingcited by 3
4 more
  1. 2024Geometric Constraints in Deep Learning Frameworks: A Surveycited by 1
  2. 2024Refinement of Monocular Depth Maps via Multi-View Differentiable Renderingcited by 1
  3. 2025Aerial Multi-View Stereo via Adaptive Depth Range Inference and Normal Cuescited by 1
  4. 2026Convolutional Neural Shading for High-Quality 3D Reconstruction from Multi-View Imagescited by 0