Signature of unique angles Histograms for 3D data description

In recent years, the local feature descriptor called SHOT (Signature of Histograms of OrienTations) has achieved excellent performance in 3D vision. However, there still exists room for improvement: on the one hand, the convexity and concavity information is still ambiguous; on the other hand, it is not consistent and unique enough for constructing histograms with just angle information between normals. In this paper, we propose a new robust descriptor called Signature of Unique Angles Histograms (SUAH) with consideration of the unique angles, which are used to construct histograms, between local reference frames. And the convexity sign is embedded to disambiguate the convexity and concavity. In addition, power normalization is used to address the point density problem. A series of experiments are conducted to show that the performance of our method is better than state-of-the-art methods on synthesized dataset and real scenes dataset.