An Efficient Plane-Segmentation Method for Indoor Point Clouds Based on Countability of Saliency Directions

Abstract

This paper proposes an efficient approach for the plane segmentation of indoor and corridor scenes. Specifically, the proposed method first uses voxels to pre-segment the scene and establishes the topological relationship between neighboring voxels. The voxel normal vectors are projected onto the surface of a Gaussian sphere based on the corresponding directions to achieve fast plane grouping using a variant of the K-means approach. To improve the segmentation integration, we propose releasing the points from the specified voxels and establishing second-order relationships between different primitives. We then introduce a global energy-optimization strategy that considers the unity and pairwise potentials while including high-order sequences to improve the over-segmentation problem. Three benchmark methods are introduced to evaluate the properties of the proposed approach by using the ISPRS benchmark datasets and self-collected in-house. The results of our experiments and the comparisons indicate that the proposed method can return reliable segmentation with precision over 72% even with the low-cost sensor, and provide the best performances in terms of the precision and recall rate compared to the benchmark methods.