# R3D-SWIN:Use Shifted Window Attention for Single-View 3D Reconstruction

arXiv: https://arxiv.org/abs/2312.02725 · HTML: https://mlsanity.com/paper/2312.02725/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 17 references that are in the map.
- First posted on arXiv 5 Dec 2023.

## Builds on (most cited first, 16 of 16)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [ShapeNet: An Information-Rich 3D Model Repository](https://mlsanity.com/paper/1512.03012/) (2015)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [SwinIR: Image Restoration Using Swin Transformer](https://mlsanity.com/paper/2108.10257/) (2021)
- [3D-R2N2: A Unified Approach for Single and Multi-view 3D Object Reconstruction](https://mlsanity.com/paper/1604.00449/) (2016)
- [Pix3D: Dataset and Methods for Single-Image 3D Shape Modeling](https://mlsanity.com/paper/1804.04610/) (2018)
- [Learning a Multi-View Stereo Machine](https://mlsanity.com/paper/1708.05375/) (2017)
- [Pix2Vox: Context-aware 3D Reconstruction from Single and Multi-view Images](https://mlsanity.com/paper/1901.11153/) (2019)
- [Pix2Vox++: Multi-scale Context-aware 3D Object Reconstruction from Single and Multiple Images](https://mlsanity.com/paper/2006.12250/) (2020)
- [Multi-view 3D Reconstruction with Transformer](https://mlsanity.com/paper/2103.12957/) (2021)
- [LegoFormer: Transformers for Block-by-Block Multi-view 3D Reconstruction](https://mlsanity.com/paper/2106.12102/) (2021)
- [3D-RETR: End-to-End Single and Multi-View 3D Reconstruction with Transformers](https://mlsanity.com/paper/2110.08861/) (2021)
- [GARNet: Global-Aware Multi-View 3D Reconstruction Network and the Cost-Performance Tradeoff](https://mlsanity.com/paper/2211.02299/) (2022)
- [UMIFormer: Mining the Correlations between Similar Tokens for Multi-View 3D Reconstruction](https://mlsanity.com/paper/2302.13987/) (2023)
- [Long-Range Grouping Transformer for Multi-View 3D Reconstruction](https://mlsanity.com/paper/2308.08724/) (2023)

## Built on by (most cited first, 1 of 1)

- [Bridging Text and Video Generation: A Survey](https://mlsanity.com/paper/2510.04999/) (2025)

## Abstract (arXiv)

Recently, vision transformers have performed well in various computer vision tasks, including voxel 3D reconstruction. However, the windows of the vision transformer are not multi-scale, and there is no connection between the windows, which limits the accuracy of voxel 3D reconstruction. Therefore, we propose a voxel 3D reconstruction network based on shifted window attention. To the best of our knowledge, this is the first work to apply shifted window attention to voxel 3D reconstruction. Experimental results on ShapeNet verify our method achieves SOTA accuracy in single-view reconstruction.