mlsanityData to 14 Oct 2026

Sub-Image Recapture for Multi-View 3D Reconstruction

arXiv 2503.06818 ↗First posted 10 Mar 2025cs.CVcited by 0

Not on the map. Why?

Abstract (arXiv)

3D reconstruction of high-resolution target remains a challenge task due to the large memory required from the large input image size. Recently developed learning based algorithms provide promising reconstruction performance than traditional ones, however, they generally require more memory than the traditional algorithms and facing scalability issue. In this paper, we developed a generic approach, sub-image recapture (SIR), to split large image into smaller sub-images and process them individually. As a result of this framework, the existing 3D reconstruction algorithms can be implemented based on sub-image recapture with significantly reduced memory and substantially improved scalability

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 16 of its 30 references that are in the map. First posted on arXiv 10 Mar 2025.

Builds on

16 of 30 references.

References · 16

  1. 2017Deformable Convolutional Networkscited by 1,488
  2. 2018MVSNet: Depth Inference for Unstructured Multi-view Stereocited by 788
  3. 2018Pyramid Stereo Matching Networkcited by 489
  4. 2019Cascade Cost Volume for High-Resolution Multi-View Stereo and Stereo Matchingcited by 417
  5. 2017End-to-End Learning of Geometry and Context for Deep Stereo Regressioncited by 360
  6. 2019Recurrent MVSNet for High-resolution Multi-view Stereo Depth Inferencecited by 254
  7. 2020PatchmatchNet: Learned Multi-View Patchmatch Stereocited by 207
  8. 2020AANet: Adaptive Aggregation Network for Efficient Stereo Matchingcited by 202
  9. 2017Learning a Multi-View Stereo Machinecited by 194
  10. 2019Cost Volume Pyramid Based Depth Inference for Multi-View Stereocited by 154
  11. 2019Point-Based Multi-View Stereo Networkcited by 147
  12. 2017SurfaceNet: An End-to-end 3D Neural Network for Multiview Stereopsiscited by 128
4 more
  1. 2020Fast-MVSNet: Sparse-to-Dense Multi-View Stereo With Learned Propagation and Gauss-Newton Refinementcited by 105
  2. 2020PVSNet: Pixelwise Visibility-Aware Multi-View Stereo Networkcited by 35
  3. 2022DS-MVSNet: Unsupervised Multi-view Stereo via Depth Synthesiscited by 9
  4. 2022Enhancing Multi-view Stereo with Contrastive Matching and Weighted Focal Losscited by 4

Built on by

Not cited yet.