QBB: Quantile-Based Binarization of 3D Point Cloud Descriptors

Local 3D point feature descriptors play an important role in many areas of computer vision, such as object recognition, registration, etc. There are many well-functioning feature descriptors, but they are typically real-valued and multidimensional vectors, leading to high computational complexity in nearest neighbor searches. To overcome this challenge, methods binarizing real-valued descriptors have emerged. In this paper, we first investigate the available binarization methods and standalone binary feature descriptors and show that existing binarization techniques cannot generally achieve good performance for arbitrary feature descriptors. To remedy this problem, we propose a new binarization method called quantile-based binarization (QBB) that can be applied to any real-valued feature descriptors. It analyses the distribution of feature descriptors that is then used to form meaningful groups along each dimension. To this end, QBB computes quantiles of the empirical distribution and the interval lengths (bin sizes) defined by quantile boundaries. Finally, it assigns a binary code to each group and concatenate them to get the final binary descriptor. QBB is able to adaptively compute the number of bits based on a capacity constraint, i.e., with the appropriate capacity setting, the resulting binary descriptor can be used on devices with lower computational power. We evaluate the descriptiveness of well-known descriptors binarized by QBB and compare them to state-of-the-art methods. According to our evaluation, QBB is able to create binary descriptors whose descriptiveness is closer to the real-valued descriptors than prior approaches. Finally, we also show that QBB can even compete with standalone binary feature descriptors.