Advanced Planar Projection Contour (PPC): A Novel Algorithm for Local Feature Description in Point Clouds

TL;DR

This paper introduces a new method for describing 3D point cloud features that is robust and efficient, even in challenging conditions.

Contribution

The novel PPC descriptor uses planar projection contours to improve robustness and efficiency in point cloud feature description.

Findings

PPC achieves over 80% accuracy across all recall levels on benchmark datasets.

The method remains effective under high-noise and varying point density conditions.

Planar projection contours provide a robust and efficient way to encode local features.

Abstract

Local feature description of point clouds is essential in 3D computer vision. However, many local feature descriptors for point clouds struggle with inadequate robustness, excessive dimensionality, and poor computational efficiency. To address these issues, we propose a novel descriptor based on Planar Projection Contours, characterized by convex packet contour information. We construct the Local Reference Frame (LRF) through covariance analysis of the query point and its neighboring points. Neighboring points are projected onto three orthogonal planes defined by the LRF. These projection points on the planes are fitted into convex hull contours and encoded as local features. These planar features are then concatenated to create the Planar Projection Contour (PPC) descriptor. We evaluated the performance of the PPC descriptor against classical descriptors using the B3R, UWAOR, and Kinect datasets. Experimental results demonstrate that the PPC descriptor achieves an accuracy exceeding 80% across all recall levels, even under high-noise and point density variation conditions, underscoring its effectiveness and robustness.

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