Signature of position angles histograms for 3D object recognition

Abstract

This paper presents a robust and rotation invariant local surface descriptor by encoding the position angles of neighboring points with a stable and unique local reference frame (LRF) into a 1D histogram. The whole procedure includes two stages: the first stage is to construct a unique LRF by performing eigenvalue decomposition on the covariance matrix formed using all the neighboring points on the local surface. On the second stage, the sphere support field of a key point was divided along the radius into several sphere shells which is similar with the Signature of Histograms OrienTations (SHOT). In each sphere shell, we calculate the cosine of the angles between the neighboring points and the x-axis, z-axis respectively to form two 1D histograms. Finally, all the 1D histograms were stitched together followed by a normalization to generate the local surface descriptor. Experiment results show that our proposed local feature descriptor is robust to noise and varying mesh-resolutions. Moreover, our local feature descriptor based 3D object recognition algorithm achieved a high average recognition rate of 98.9% on the whole UWA dataset.