Indoor 3D Point Cloud Segmentation Based on Multi-Constraint Graph Clustering

Abstract

Indoor scene point cloud segmentation plays an essential role in 3D reconstruction and scene classification. This paper proposes a multi-constraint graph clustering method (MCGC) for indoor scene segmentation. The MCGC method considers multi-constraints, including extracted structural planes, local surface convexity, and color information of objects for indoor segmentation. Firstly, the raw point cloud is partitioned into surface patches, and we propose a robust plane extraction method to extract the main structural planes of the indoor scene. Then, the match between the surface patches and the structural planes is achieved by global energy optimization. Next, we closely integrate multiple constraints mentioned above to design a graph clustering algorithm to partition cluttered indoor scenes into object parts. Finally, we present a post-refinement step to filter outliers. We conducted experiments on a benchmark RGB-D dataset and a real indoor laser-scanned dataset to perform numerous qualitative and quantitative evaluation experiments, the results of which have verified the effectiveness of the MCGC method. Compared with state-of-the-art methods, MCGC can deal with the segmentation of indoor scenes more efficiently and restore more details of indoor structures. The segment precision and the segment recall of experimental results reach 70% on average. In addition, a great advantage of the MCGC method is that the speed of processing point clouds is very fast; it takes about 1.38 s to segment scene data of 1 million points. It significantly reduces the computation overhead of scene point cloud data and achieves real-time scene segmentation.

Segmentation of 3D Point Clouds of Heritage Buildings using Edge Detection and Supervoxel-Based Topology

“…Other approaches exist for planar region extraction, apart from RANSAC. Luo et al [16] use a deterministic method [17] to detect planes in noisy and unorganized 3D indoor scenes. After the planar extraction task, this approach examines the normalized distance between patches and surfaces before implementing a multi-constraint approach to a structured graph.…”

“…2 algorithms [10][11][12], or model fitting [13][14][15][16][17]. Classification often uses Machine Learning (ML) algorithms such as Support Vector Machine (SVM) [18] or Random Forest (RF) [9,19].…”