Robust and Accurate Feature Detection on Point Clouds

Abstract

Geometric feature detection on surfaces is a crucial task for the characterization and understanding of geometry shapes. In this paper, we present a robust and reliable approach for accurately capturing local surface variations at different feature sizes within point clouds. To this end, we define a bilateral weighted centroid projection-based metric to quantify surface deviations. Based on the metric, we propose a structure-to-detail feature perception algorithm to accurately locate geometric features of varying sizes. Additionally, we use tensor analysis to extract boundary features. We evaluate our method on various object- and scene-level point clouds, demonstrating its superiority and versatility over existing techniques. Computational results show that our method is capable of identifying a wide range of geometric characteristics within point clouds, including complicated structures, rich textures, fine details, shallow curves, and geometric boundaries. We also validate the effectiveness of our approach on several downstream applications, including segmentation, surface reconstruction, and feature line extraction.