# Indirect Point Cloud Registration: Aligning Distance Fields using a Pseudo Third Point Ses

arXiv: https://arxiv.org/abs/2205.15954 · HTML: https://mlsanity.com/paper/2205.15954/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 May 2022.

## Built on by (most cited first, 3 of 3)

- [An Algorithm for the SE(3)-Transformation on Neural Implicit Maps for Remapping Functions](https://mlsanity.com/paper/2206.08712/) (2022)
- [NSLF-OL: Online Learning of Neural Surface Light Fields alongside Real-time Incremental 3D Reconstruction](https://mlsanity.com/paper/2305.00282/) (2023)
- [SDFReg: Learning Signed Distance Functions for Point Cloud Registration](https://mlsanity.com/paper/2304.08929/) (2023)

## Abstract (arXiv)

In recent years, implicit functions have drawn attention in the field of 3D reconstruction and have successfully been applied with Deep Learning. However, for incremental reconstruction, implicit function-based registrations have been rarely explored. Inspired by the high precision of deep learning global feature registration, we propose to combine this with distance fields. We generalize the algorithm to a non-Deep Learning setting while retaining the accuracy. Our algorithm is more accurate than conventional models while, without any training, it achieves a competitive performance and faster speed, compared to Deep Learning-based registration models. The implementation is available on github for the research community.