Salient Local 3D Features for 3D Shape Retrieval

TL;DR

This paper introduces a novel 3D salient local feature extraction method based on voxel grids inspired by SIFT, enabling effective shape retrieval across various 3D model types.

Contribution

It presents a new voxel-based formulation for 3D salient features and applies a bag of words approach for shape retrieval, applicable to rigid, articulated, and deformable models.

Findings

Effective retrieval on McGill shape benchmark

Comparable or improved accuracy over existing methods

Applicable to diverse 3D model types

Abstract

In this paper we describe a new formulation for the 3D salient local features based on the voxel grid inspired by the Scale Invariant Feature Transform (SIFT). We use it to identify the salient keypoints (invariant points) on a 3D voxelized model and calculate invariant 3D local feature descriptors at these keypoints. We then use the bag of words approach on the 3D local features to represent the 3D models for shape retrieval. The advantages of the method are that it can be applied to rigid as well as to articulated and deformable 3D models. Finally, this approach is applied for 3D Shape Retrieval on the McGill articulated shape benchmark and then the retrieval results are presented and compared to other methods.