Enhanced 3D Shape Analysis via Information Geometry

TL;DR

This paper introduces an information geometric framework for 3D point cloud shape analysis using Gaussian Mixture Models, with a new divergence measure that is stable and effectively captures geometric differences.

Contribution

It proposes the Modified Symmetric Kullback-Leibler divergence on GMMs, providing stable, bounded comparisons for 3D shape analysis, improving over traditional metrics.

Findings

MSKL outperforms traditional distances in stability and sensitivity.

Experiments on human and animal datasets validate the effectiveness of MSKL.

The framework ensures numerical stability for GMM comparisons.

Abstract

Three-dimensional point clouds provide highly accurate digital representations of objects, essential for applications in computer graphics, photogrammetry, computer vision, and robotics. However, comparing point clouds faces significant challenges due to their unstructured nature and the complex geometry of the surfaces they represent. Traditional geometric metrics such as Hausdorff and Chamfer distances often fail to capture global statistical structure and exhibit sensitivity to outliers, while existing Kullback-Leibler (KL) divergence approximations for Gaussian Mixture Models can produce unbounded or numerically unstable values. This paper introduces an information geometric framework for 3D point cloud shape analysis by representing point clouds as Gaussian Mixture Models (GMMs) on a statistical manifold. We prove that the space of GMMs forms a statistical manifold and propose the Modified Symmetric Kullback-Leibler (MSKL) divergence with theoretically guaranteed upper and lower bounds, ensuring numerical stability for all GMM comparisons. Through comprehensive experiments on human pose discrimination (MPI-FAUST dataset) and animal shape comparison (G-PCD dataset), we demonstrate that MSKL provides stable and monotonically varying values that directly reflect geometric variation, outperforming traditional distances and existing KL approximations.