# A Benchmark and Evaluation of Non-Rigid Structure from Motion

arXiv: https://arxiv.org/abs/1801.08388 · HTML: https://mlsanity.com/paper/1801.08388/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jan 2018.

## Built on by (most cited first, 14 of 14)

- [Neural Trajectory Fields for Dynamic Novel View Synthesis](https://mlsanity.com/paper/2105.05994/) (2021)
- [Consistent Depth of Moving Objects in Video](https://mlsanity.com/paper/2108.01166/) (2021)
- [State of the Art in Dense Monocular Non-Rigid 3D Reconstruction](https://mlsanity.com/paper/2210.15664/) (2022)
- [Virtual Correspondence: Humans as a Cue for Extreme-View Geometry](https://mlsanity.com/paper/2206.08365/) (2022)
- [Organic Priors in Non-Rigid Structure from Motion](https://mlsanity.com/paper/2207.06262/) (2022)
- [Fast Encoder-Based 3D from Casual Videos via Point Track Processing](https://mlsanity.com/paper/2404.07097/) (2024)
- [The Drunkard's Odometry: Estimating Camera Motion in Deforming Scenes](https://mlsanity.com/paper/2306.16917/) (2023)
- [View Birdification in the Crowd: Ground-Plane Localization from Perceived Movements](https://mlsanity.com/paper/2111.05060/) (2021)
- [High Fidelity 3D Reconstructions with Limited Physical Views](https://mlsanity.com/paper/2110.11599/) (2021)
- [Controllable GAN Synthesis Using Non-Rigid Structure-from-Motion](https://mlsanity.com/paper/2211.07195/) (2022)
- [Non-Rigid Structure-from-Motion via Differential Geometry with Recoverable Conformal Scale](https://mlsanity.com/paper/2510.01665/) (2025)
- [HeadRecon: High-Fidelity 3D Head Reconstruction from Monocular Video](https://mlsanity.com/paper/2312.08863/) (2023)
- [Non-rigid Structure-from-Motion: Temporally-smooth Procrustean Alignment and Spatially-variant Deformation Modeling](https://mlsanity.com/paper/2405.04309/) (2024)
- [3D Reconstruction of the Human Colon from Capsule Endoscope Video](https://mlsanity.com/paper/2407.15228/) (2024)

## Abstract (arXiv)

Non-Rigid structure from motion (NRSfM), is a long standing and central problem in computer vision and its solution is necessary for obtaining 3D information from multiple images when the scene is dynamic. A main issue regarding the further development of this important computer vision topic, is the lack of high quality data sets. We here address this issue by presenting a data set created for this purpose, which is made publicly available, and considerably larger than the previous state of the art. To validate the applicability of this data set, and provide an investigation into the state of the art of NRSfM, including potential directions forward, we here present a benchmark and a scrupulous evaluation using this data set. This benchmark evaluates 18 different methods with available code that reasonably spans the state of the art in sparse NRSfM. This new public data set and evaluation protocol will provide benchmark tools for further development in this challenging field.