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...

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