Rotational Voxels Statistics Histogram for both real-valued and binary feature representations of 3D local shape

• Published in: Journal of visual communication and image representation 2023-05, Vol.93, p.103817, Article 103817
• Main Authors: Hao, Linbo, Yang, Xuefeng, Xu, Ke, Yi, Wentao, Shen, Ying, Wang, Huaming
• Format: Article
• Language: English
• Subjects: 3D local feature descriptor; 3D multi-pose processing mechanism; Binary extension; Point cloud
• ISSN: 1047-3203; 1095-9076
• DOI: 10.1016/j.jvcir.2023.103817

3D Local feature description is an active and fundamental task in 3D computer vision. However, most of the existing descriptors fail to simultaneously achieve satisfactory performance among descriptiveness, robustness, efficiency, and compactness. To address these limitations, we first propose a real-valued descriptor named Rotational Voxels Statistics Histogram (RoVo), which exploits the novel 3D multi-pose processing mechanism proposed in this paper to calculate the 3D voxel density distribution in different 3D poses. Moreover, through well-designed binary encoding algorithms, we conduct the seamless extension of the real-valued RoVo descriptor to three binary representations that have different performance characteristics. Extensive evaluation experiments validate the superiority of the real-valued and three binary RoVo descriptors concerning descriptiveness, robustness, and efficiency. Furthermore, the three binary RoVo descriptors extend the performance of high compactness. Lastly, we perform the experiments of 3D scene registration and 3D object recognition to intuitively present the effectiveness of the four proposed RoVo descriptors. •We propose the novel multi-pose processing mechanism for 3D feature description.•Based on the new mechanism, we propose a real-valued descriptor named RoVo.•We seamlessly extend the real-valued RoVo descriptor to three binary representations.•Extensive experiments validate the superiority of the four RoVo descriptors.