Effective multi-view registration of point sets based on student's t mixture model

TL;DR

This paper introduces a robust multi-view registration method using Student's t Mixture Model, effectively handling noise and outliers, and demonstrating superior accuracy and robustness over existing techniques.

Contribution

The paper proposes a novel multi-view registration approach based on Student's t Mixture Model, improving robustness to noise and outliers compared to Gaussian-based methods.

Findings

Outperforms state-of-the-art methods in robustness and accuracy.

Efficient registration via nearest neighbor search.

Inherently robust to heavy-tailed noise and outliers.

Abstract

Recently, Expectation-maximization (EM) algorithm has been introduced as an effective means to solve multi-view registration problem. Most of the previous methods assume that each data point is drawn from the Gaussian Mixture Model (GMM), which is difficult to deal with the noise with heavy-tail or outliers. Accordingly, this paper proposed an effective registration method based on Student's t Mixture Model (StMM). More specially, we assume that each data point is drawn from one unique StMM, where its nearest neighbors (NNs) in other point sets are regarded as the t-distribution centroids with equal covariances, membership probabilities, and fixed degrees of freedom. Based on this assumption, the multi-view registration problem is formulated into the maximization of the likelihood function including all rigid transformations. Subsequently, the EM algorithm is utilized to optimize rigid transformations as well as the only t-distribution covariance for multi-view registration. Since only a few model parameters require to be optimized, the proposed method is more likely to obtain the desired registration results. Besides, all t-distribution centroids can be obtained by the NN search method, it is very efficient to achieve multi-view registration. What's more, the t-distribution takes the noise with heavy-tail into consideration, which makes the proposed method be inherently robust to noises and outliers. Experimental results tested on benchmark data sets illustrate its superior performance on robustness and accuracy over state-of-the-art methods.