A Study on Topological Descriptors for the Analysis of 3D Surface Texture

TL;DR

This paper explores the use of topological descriptors for analyzing 3D surface textures, demonstrating their effectiveness and complementarity with existing methods, leading to improved classification performance.

Contribution

It provides a comprehensive evaluation of topological descriptors in 3D surface analysis and shows their potential to enhance classification accuracy when combined with other descriptors.

Findings

Topological descriptors reflect class-specific surface information.

They can compete with state-of-the-art non-topological descriptors.

Combining topological and non-topological descriptors improves classification results.

Abstract

Methods from computational topology are becoming more and more popular in computer vision and have shown to improve the state-of-the-art in several tasks. In this paper, we investigate the applicability of topological descriptors in the context of 3D surface analysis for the classification of different surface textures. We present a comprehensive study on topological descriptors, investigate their robustness and expressiveness and compare them with state-of-the-art methods including Convolutional Neural Networks (CNNs). Results show that class-specific information is reflected well in topological descriptors. The investigated descriptors can directly compete with non-topological descriptors and capture complementary information. As a consequence they improve the state-of-the-art when combined with non-topological descriptors.