3D interest point detection using balance-distortion oriented selection

Interest point detection is a challenging problem in 3D objects. Compared to traditional corner detection based on the curvature, this paper proposes a novel method that quantifies the balance and uniformity of local geometric structures based on the distribution of vertex neighborhoods. We first define the neighborhoods of vertices and structure them within the two-ring, instead of constructing the overall mesh, so as to avoid the interference between the neighborhoods of different vertices. Then we introduce the concept "balance-distortion" to describe the geometric features of the local structure. The experimental results show that the proposed algorithm is robust against noise and invariant to geometric transformation. In addition, compared with the corner detection, more feature points that do not satisfy the balance and direction uniformity are detected, and the distribution of interest point is more uniform.