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Several approaches for interest point detection on rigid shapes have been proposed, but few are available for non-rigid shapes. It is a very challenging task due to the presence of the large degrees of local deformations. This paper presents a novel method called persistence-based heat kernel signature (pHKS). It consists of two steps: Scalar field construction and interest point detection. We propose to use the heat kernel signature function at a moderately small time scale to construct the scalar field. It has the advantage of being stable under various transformations. Based on the predefined scalar field, a 0-dimensional persistence diagram is computed, and the local geometric and global structural information of the shape are captured at the same time. Points with local maxima and high persistence are selected as interest points. We perform a comprehensive evaluation on two popular datasets (i.e., PHOTOMESH and Interest Points Dataset) to show the effectiveness of our method. Compared with existing techniques, our interest point detector achieves a superior performance in terms of repeatability and distinctiveness.

Persistence-based Interest Point Detection for 3D Deformable Surface