Laplacian Mixture Model Point Based Registration

TL;DR

This paper introduces a novel point set registration method based on Laplacian mixture models, framing the alignment as a probability density estimation problem and using maximum likelihood for fitting.

Contribution

It presents a new Laplacian mixture model approach for point registration, offering an alternative to existing methods by modeling data with Laplacian distributions.

Findings

Effective in aligning point sets in various applications

Provides a probabilistic framework for registration

Demonstrates competitive accuracy with existing methods

Abstract

Point base registration is an important part in many machine VISIOn applications, medical diagnostics, agricultural studies etc. The goal of point set registration is to find correspondences between different data sets and estimate the appropriate transformation that can map one set to another. Here we introduce a novel method for matching of different data sets based on Laplacian distribution. We consider the alignment of two point sets as probability density estimation problem. By using maximum likelihood methods we try to fit the Laplacian mixture model (LMM) centroids (source point set) to the data point set.