# An Adaptive Data Representation for Robust Point-Set Registration and Merging

arXiv: https://arxiv.org/abs/1511.04240 · HTML: https://mlsanity.com/paper/1511.04240/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Nov 2015.

## Built on by (most cited first, 11 of 11)

- [Unsupervised Deep Probabilistic Approach for Partial Point Cloud Registration](https://mlsanity.com/paper/2303.13290/) (2023)
- [LSG-CPD: Coherent Point Drift with Local Surface Geometry for Point Cloud Registration](https://mlsanity.com/paper/2103.15039/) (2021)
- [Overlap-guided Gaussian Mixture Models for Point Cloud Registration](https://mlsanity.com/paper/2210.09836/) (2022)
- [Correspondence-Free SE(3) Point Cloud Registration in RKHS via Unsupervised Equivariant Learning](https://mlsanity.com/paper/2407.20223/) (2024)
- [Hybrid Trilinear and Bilinear Programming for Aligning Partially Overlapping Point Sets](https://mlsanity.com/paper/2101.07458/) (2021)
- [An Improved Discriminative Optimization for 3D Rigid Point Cloud Registration](https://mlsanity.com/paper/2104.08854/) (2021)
- [Fast Simultaneous Gravitational Alignment of Multiple Point Sets](https://mlsanity.com/paper/2106.11308/) (2021)
- [BO-ICP: Initialization of Iterative Closest Point Based on Bayesian Optimization](https://mlsanity.com/paper/2304.13114/) (2023)
- [Decomposed Global Optimization for Robust Point Matching with Low-Dimensional Branching](https://mlsanity.com/paper/2405.08589/) (2024)
- [Attention-guided reference point shifting for Gaussian-mixture-based partial point set registration](https://mlsanity.com/paper/2512.02496/) (2025)
- [Scalable and Differentiable Point-Cloud Registration Using Maximum Mean Discrepancy](https://mlsanity.com/paper/2606.27818/) (2026)

## Abstract (arXiv)

This paper presents a framework for rigid point-set registration and merging using a robust continuous data representation. Our point-set representation is constructed by training a one-class support vector machine with a Gaussian radial basis function kernel and subsequently approximating the output function with a Gaussian mixture model. We leverage the representation's sparse parametrisation and robustness to noise, outliers and occlusions in an efficient registration algorithm that minimises the L2 distance between our support vector--parametrised Gaussian mixtures. In contrast, existing techniques, such as Iterative Closest Point and Gaussian mixture approaches, manifest a narrower region of convergence and are less robust to occlusions and missing data, as demonstrated in the evaluation on a range of 2D and 3D datasets. Finally, we present a novel algorithm, GMMerge, that parsimoniously and equitably merges aligned mixture models, allowing the framework to be used for reconstruction and mapping.