# Sub-Image Recapture for Multi-View 3D Reconstruction

arXiv: https://arxiv.org/abs/2503.06818 · HTML: https://mlsanity.com/paper/2503.06818/

## 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 (most cited first, 16 of 16)

- [Deformable Convolutional Networks](https://mlsanity.com/paper/1703.06211/) (2017)
- [MVSNet: Depth Inference for Unstructured Multi-view Stereo](https://mlsanity.com/paper/1804.02505/) (2018)
- [Pyramid Stereo Matching Network](https://mlsanity.com/paper/1803.08669/) (2018)
- [Cascade Cost Volume for High-Resolution Multi-View Stereo and Stereo Matching](https://mlsanity.com/paper/1912.06378/) (2019)
- [End-to-End Learning of Geometry and Context for Deep Stereo Regression](https://mlsanity.com/paper/1703.04309/) (2017)
- [Recurrent MVSNet for High-resolution Multi-view Stereo Depth Inference](https://mlsanity.com/paper/1902.10556/) (2019)
- [PatchmatchNet: Learned Multi-View Patchmatch Stereo](https://mlsanity.com/paper/2012.01411/) (2020)
- [AANet: Adaptive Aggregation Network for Efficient Stereo Matching](https://mlsanity.com/paper/2004.09548/) (2020)
- [Learning a Multi-View Stereo Machine](https://mlsanity.com/paper/1708.05375/) (2017)
- [Cost Volume Pyramid Based Depth Inference for Multi-View Stereo](https://mlsanity.com/paper/1912.08329/) (2019)
- [Point-Based Multi-View Stereo Network](https://mlsanity.com/paper/1908.04422/) (2019)
- [SurfaceNet: An End-to-end 3D Neural Network for Multiview Stereopsis](https://mlsanity.com/paper/1708.01749/) (2017)
- [Fast-MVSNet: Sparse-to-Dense Multi-View Stereo With Learned Propagation and Gauss-Newton Refinement](https://mlsanity.com/paper/2003.13017/) (2020)
- [PVSNet: Pixelwise Visibility-Aware Multi-View Stereo Network](https://mlsanity.com/paper/2007.07714/) (2020)
- [DS-MVSNet: Unsupervised Multi-view Stereo via Depth Synthesis](https://mlsanity.com/paper/2208.06674/) (2022)
- [Enhancing Multi-view Stereo with Contrastive Matching and Weighted Focal Loss](https://mlsanity.com/paper/2206.10360/) (2022)

## 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