On shortened 3D local binary descriptors

The wide-spread mobile systems nowadays desire ultra lightweight local geometric features to accomplish tasks relying on correspondences. Nonetheless, most existing 3D local feature descriptors, though shown to be distinctive and robust, still are real-valued and/or high-dimensional. Accordingly, this paper conducts a comparative study on current bit-selection methods with a focus on shortening 3D local binary descriptors. By analyzing several bit-selection techniques, we develop and evaluate various approaches to obtain a shortened version of a state-of-the-art feature remaining discriminative and robust. Through extensive experiments on four standard datasets with different data modalities (e.g., LiDAR and Kinect) and application scenarios (e.g., 3D object retrieval, 3D object recognition, and point cloud registration), we show that a small subset of representative bits are sufficient to achieve promising feature matching results as the initial descriptor. Moreover, the shortened binary descriptors still hold competitive or better distinctiveness and robustness compared to several state-of-the-art real-valued descriptors, e.g., spin image, SHOT, and RoPS, albeit being dramatically more efficient to match and store. Key to the foreseen research trend of local geometric feature description is dealing with compact binary descriptors; thus, our work may pave the way for this new research direction.