# Registration Techniques for Deformable Objects

arXiv: https://arxiv.org/abs/2111.04053 · HTML: https://mlsanity.com/paper/2111.04053/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 57 references that are in the map.
- First posted on arXiv 7 Nov 2021.

## Builds on (most cited first, 5 of 5)

- [ReFusion: 3D Reconstruction in Dynamic Environments for RGB-D Cameras Exploiting Residuals](https://mlsanity.com/paper/1905.02082/) (2019)
- [VolumeDeform: Real-time Volumetric Non-rigid Reconstruction](https://mlsanity.com/paper/1603.08161/) (2016)
- [A compact formula for the derivative of a 3-D rotation in exponential coordinates](https://mlsanity.com/paper/1312.0788/) (2013)
- [Capturing Dynamic Textured Surfaces of Moving Targets](https://mlsanity.com/paper/1604.02801/) (2016)
- [A General Framework for Flexible Multi-Cue Photometric Point Cloud Registration](https://mlsanity.com/paper/1709.05945/) (2017)

## Abstract (arXiv)

In general, the problem of non-rigid registration is about matching two different scans of a dynamic object taken at two different points in time. These scans can undergo both rigid motions and non-rigid deformations. Since new parts of the model may come into view and other parts get occluded in between two scans, the region of overlap is a subset of both scans. In the most general setting, no prior template shape is given and no markers or explicit feature point correspondences are available. So, this case is a partial matching problem that takes into account the assumption that consequent scans undergo small deformations while having a significant amount of overlapping area [28]. The problem which this thesis is addressing is mapping deforming objects and localizing cameras in the environment at the same time.