BRoPH: An efficient and compact binary descriptor for 3D point clouds. (April 2018)

• Record Type: Journal Article
• Authors: Zou, Yu; Wang, Xueqian; Zhang, Tao; Liang, Bin; Song, Jingyan; Liu, Houde
• Is Part Of: Pattern recognition. Volume 76(2018:Apr.)
• Page Start: 522
• Page End: 536
• Publication Date: 2018-04
• Subjects: Local reference frame -- Binary feature descriptor -- Object recognition -- Multi-view&multi-scale; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2017.11.029
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: A repeatable and stable Local Reference Frame is presented for descriptor generation. A novel binary local descriptor called BRoPH is proposed for 3D point clouds. Extensive experiments on four datasets with various data qualities are carried out. BRoPH gets the best efficient and compact results against selected floating methods. Abstract: 3D feature descriptor plays an essential role in 3D computer vision as it is a pre-requisite step for many 3D vision applications. Despite there exists many 3D feature descriptors currently, they are mostly represented in floating representation, resulting costly computation and storage. In this paper, we propose a 3D binary local feature descriptor, Binary Rotational Projection Histogram (BRoPH), aimed at compactness of representation and efficiency of computation. BRoPH is generated directly from point cloud by turning the description of 3D point cloud into a series binarization of 2D image patches. The exploited local reference frame promotes the construction efficiency meanwhile maintains repeatability and stability, the multi-view mechanism and integration of density distribution and depth information employed in BRoPH complement each other and enhance its descriptiveness, and the multi-scale extension of Center-Symmetric Local Binary Patterns (CS-LBP) provides an efficient and compact way to generate binary string. We compare BRoPH against several representative descriptors on public datasets and demonstrate that it achieves about 14 times more compact, 28 and 4 times more faster in terms of describing and matching time respectively, than the average performance of the compared floating descriptors.