Signature of Geometric Centroids for 3D Local Shape Description and Partial Shape Matching

TL;DR

This paper introduces a novel 3D shape descriptor based on geometric centroids that enables direct, robust local shape matching from depth scans without preprocessing, improving object recognition and reconstruction tasks.

Contribution

The paper presents a new Signature of Geometric Centroids descriptor that supports direct matching of partial and noisy 3D scans without preprocessing or mesh conversion.

Findings

Robustness to noise, occlusion, and varying point density

Outperforms three state-of-the-art descriptors in shape matching

Effective in object/scene reconstruction and recognition

Abstract

Depth scans acquired from different views may contain nuisances such as noise, occlusion, and varying point density. We propose a novel Signature of Geometric Centroids descriptor, supporting direct shape matching on the scans, without requiring any preprocessing such as scan denoising or converting into a mesh. First, we construct the descriptor by voxelizing the local shape within a uniquely defined local reference frame and concatenating geometric centroid and point density features extracted from each voxel. Second, we compare two descriptors by employing only corresponding voxels that are both non-empty, thus supporting matching incomplete local shape such as those close to scan boundary. Third, we propose a descriptor saliency measure and compute it from a descriptor-graph to improve shape matching performance. We demonstrate the descriptor's robustness and effectiveness for shape matching by comparing it with three state-of-the-art descriptors, and applying it to object/scene reconstruction and 3D object recognition.