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Local feature description is a fundamental yet challenging task in 3Dcomputer vision. This paper proposes a novel descriptor, named Statistic ofDeviation Angles on Subdivided Space (SDASS), of encoding geometrical andspatial information of local surface on Local Reference Axis (LRA). In terms ofencoding geometrical information, considering that surface normals, which areusually used for encoding geometrical information of local surface, arevulnerable to various nuisances (e.g., noise, varying mesh resolutions etc.),we propose a robust geometrical attribute, called Local Minimum Axis (LMA), toreplace the normals for generating the geometrical feature in our SDASSdescriptor. For encoding spatial information, we use two spatial features forfully encoding the spatial information of a local surface based on LRA whichusually presents high overall repeatability than Local Reference Axis (LRF).Besides, an improved LRA is proposed for increasing the robustness of our SDASSto noise and varying mesh resolutions. The performance of the SDASS descriptoris rigorously tested on four popular datasets. The results show that ourdescriptor has a high descriptiveness and strong robustness, and itsperformance outperform existing algorithms by a large margin. Finally, theproposed descriptor is applied to 3D registration. The accurate result furtherconfirms the effectiveness of our SDASS method.

A Novel SDASS Descriptor for Fully Encoding the Information of 3D Local Surface