Efficient Image-Space Extraction and Representation of 3D Surface Topography

TL;DR

This paper introduces an efficient image-space method for extracting and enhancing 3D surface topography from high-resolution scans, improving classification accuracy by filtering noise and emphasizing topographic features.

Contribution

The paper presents a novel image-space technique for surface topography extraction that enhances attribute representation and outperforms existing 2D and 3D methods in classification tasks.

Findings

Improved topographic attribute capture

Significant classification performance boost

Effective noise filtering and attribute enhancement

Abstract

Surface topography refers to the geometric micro-structure of a surface and defines its tactile characteristics (typically in the sub-millimeter range). High-resolution 3D scanning techniques developed recently enable the 3D reconstruction of surfaces including their surface topography. In his paper, we present an efficient image-space technique for the extraction of surface topography from high-resolution 3D reconstructions. Additionally, we filter noise and enhance topographic attributes to obtain an improved representation for subsequent topography classification. Comprehensive experiments show that the our representation captures well topographic attributes and significantly improves classification performance compared to alternative 2D and 3D representations.