A Novel SDASS Descriptor for Fully Encoding the Information of 3D Local Surface

TL;DR

This paper introduces SDASS, a robust 3D local surface descriptor that encodes geometrical and spatial information using a new geometrical attribute and improved reference axes, outperforming existing methods.

Contribution

The paper presents a novel SDASS descriptor with a new geometrical attribute (LMA) and an improved reference axis for enhanced robustness and descriptiveness in 3D surface analysis.

Findings

Outperforms existing descriptors on four datasets.

Demonstrates high robustness to noise and mesh resolution changes.

Effective in 3D registration tasks.

Abstract

Local feature description is a fundamental yet challenging task in 3D computer vision. This paper proposes a novel descriptor, named Statistic of Deviation Angles on Subdivided Space (SDASS), of encoding geometrical and spatial information of local surface on Local Reference Axis (LRA). In terms of encoding geometrical information, considering that surface normals, which are usually used for encoding geometrical information of local surface, are vulnerable to various nuisances (e.g., noise, varying mesh resolutions etc.), we propose a robust geometrical attribute, called Local Minimum Axis (LMA), to replace the normals for generating the geometrical feature in our SDASS descriptor. For encoding spatial information, we use two spatial features for fully encoding the spatial information of a local surface based on LRA which usually presents high overall repeatability than Local Reference Axis (LRF). Besides, an improved LRA is proposed for increasing the robustness of our SDASS to noise and varying mesh resolutions. The performance of the SDASS descriptor is rigorously tested on four popular datasets. The results show that our descriptor has a high descriptiveness and strong robustness, and its performance outperform existing algorithms by a large margin. Finally, the proposed descriptor is applied to 3D registration. The accurate result further confirms the effectiveness of our SDASS method.