# NRMVS: Non-Rigid Multi-View Stereo

arXiv: https://arxiv.org/abs/1901.03910 · HTML: https://mlsanity.com/paper/1901.03910/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jan 2019.

## Built on by (most cited first, 5 of 5)

- [Dynamic Point Fields](https://mlsanity.com/paper/2304.02626/) (2023)
- [Learning to Fuse Monocular and Multi-view Cues for Multi-frame Depth Estimation in Dynamic Scenes](https://mlsanity.com/paper/2304.08993/) (2023)
- [SIMBAR: Single Image-Based Scene Relighting For Effective Data Augmentation For Automated Driving Vision Tasks](https://mlsanity.com/paper/2204.00644/) (2022)
- [ZeST-NeRF: Using temporal aggregation for Zero-Shot Temporal NeRFs](https://mlsanity.com/paper/2311.18491/) (2023)
- [Camera Movement Estimation and Path Correction using the Combination of Modified A-SIFT and Stereo System for 3D Modelling](https://mlsanity.com/paper/2503.17668/) (2025)

## Abstract (arXiv)

Scene reconstruction from unorganized RGB images is an important task in many computer vision applications. Multi-view Stereo (MVS) is a common solution in photogrammetry applications for the dense reconstruction of a static scene. The static scene assumption, however, limits the general applicability of MVS algorithms, as many day-to-day scenes undergo non-rigid motion, e.g., clothes, faces, or human bodies. In this paper, we open up a new challenging direction: dense 3D reconstruction of scenes with non-rigid changes observed from arbitrary, sparse, and wide-baseline views. We formulate the problem as a joint optimization of deformation and depth estimation, using deformation graphs as the underlying representation. We propose a new sparse 3D to 2D matching technique, together with a dense patch-match evaluation scheme to estimate deformation and depth with photometric consistency. We show that creating a dense 4D structure from a few RGB images with non-rigid changes is possible, and demonstrate that our method can be used to interpolate novel deformed scenes from various combinations of these deformation estimates derived from the sparse views.