Abstract
Growing availability and use of 3D data acquisition devices have spurred interest in 3D vision field. Feature descriptor is essential to feature matching. Current 3D feature descriptors are mostly represented in float vector. In this paper, we propose a binary 3D feature descriptor, called BRoPH, by turning the binary description of the 3D point cloud into a series of binarization of projected 2D image patches. We demonstrate through experiments that BRoPH achieves a comparable descriptiveness and robustness with respect to the state-of-the-art 3D feature descriptors while outperforms the others in terms of compactness and efficiency.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
