SGLBP: Subgraph‐based Local Binary Patterns for Feature Extraction on Point Clouds

Abstract

Extraction for points that can outline the shape of a point cloud is an important task for point cloud processing in various applications. The topology information of the neighbourhood of a point usually contains sufficient information for detecting features, which is fully considered in this study. Therefore, a novel method for extracting feature points based on the topology information is proposed. First, an improved ‐shape technique is introduced, generating two graphs for potential feature detection and neighbourhood description, respectively. Local binary pattern (LBP) is then applied to the subgraphs, thus subgraph‐based local binary patterns (SGLBPs) are generated for encoding the topology of the neighbourhoods of points, which helps to remove non‐feature points from potential feature points. The proposed method can directly process raw point clouds and needs no prior surface reconstruction or geometric invariants computation; furthermore, the proposed method detects feature points by analysing the topologies of the neighbourhoods of points, consequently promoting the effectiveness for tiny features and the robustness to noises and non‐uniformly sampling patterns. The experimental results demonstrate that the proposed method is robust and achieves state‐of‐the‐art performance.