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

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arxiv 2106.15328 v2 pith:UPIDS2PZ submitted 2021-06-18 cs.CV

Qingtian Zhu , Chen Min , Zizhuang Wei , Yisong Chen , Guoping Wang This is my paper · ORCID
classification cs.CV
keywords deeplearningreconstructionfuturemulti-viewpresentsresearchstereo
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 3 Pith papers

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  1. Geometry Field Splatting with Gaussian Surfels

    cs.GR 2024-11 conditional novelty 7.0 of 10

    Gaussian surfels can parameterize a stochastic geometry field with a closed-form, near-exact differentiable splatting renderer, improving 3D surface reconstruction on DTU and BlendedMVS.

  2. Aerial Multi-View Stereo via Adaptive Depth Range Inference and Normal Cues

    cs.CV 2025-06 conditional novelty 6.0 of 10

    Aerial depth estimation improves by using monocular depth and normals to predict adaptive depth ranges, boosting SOTA accuracy on WHU, LuoJia-MVS, and München.

  3. Convolutional Neural Shading for High-Quality 3D Reconstruction from Multi-View Images

    cs.CV 2026-07 conditional novelty 5.0 of 10

    Convolutional neural shading plus a fine-detail displacement network reconstructs multi-view 3D meshes with lower Chamfer distance than Neuralangelo and related neural surface methods, especially on dark and sparse-vi...

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