Direct Fitting of Gaussian Mixture Models - Robotics Institute Carnegie Mellon University

Direct Fitting of Gaussian Mixture Models

Conference Paper, Proceedings of 16th Conference on Computer and Robot Vision (CRV '19), pp. 25 - 32, May, 2019

Abstract

When fitting Gaussian Mixture Models to 3D geometry, the model is typically fit to point clouds, even when the shapes were obtained as 3D meshes. Here we present a formulation for fitting Gaussian Mixture Models (GMMs) directly to geometric objects, using the triangles of triangular mesh instead of using points sampled from its surface. We demonstrate that this modification enables fitting higher-quality GMMs under a wider range of initialization conditions. Additionally, models obtained from this fitting method are shown to produce an improvement in 3D registration for both meshes and RGB-D frames.

BibTeX

@conference{Keselman-2019-122790,
author = {Leonid Keselman and Martial Hebert},
title = {Direct Fitting of Gaussian Mixture Models},
booktitle = {Proceedings of 16th Conference on Computer and Robot Vision (CRV '19)},
year = {2019},
month = {May},
pages = {25 - 32},
}