A Physicist's View on Partial 3D Shape Matching

A new algorithm is presented to compute nonrigid, possibly partial comparisons of shapes defined by unstructured triangulations of their surfaces. The algorithm takes as input a pair of surfaces with each surface given by a distinct and unrelated triangulation. Its goal is to define a possibly partial correspondence between the vertices of the two triangulations, with a cost associated with this correspondence that can serve as a measure of the similarity of the two shapes. To find this correspondence, the vertices in each triangulation are characterized by a signature vector of features. We tested both the LD-SIFT signatures, based on the concept of spin images, and the wave kernel signatures obtained by solving the Shrodinger equation on the triangulation. A cost matrix C is constructed such that C(k,l) is the norm of the difference of the signature vectors of vertices k and l. The correspondence between the triangulations is then computed as the transport plan that solves the optimal transport or optimal partial transport problem between their sets of vertices. We use a statistical physics approach to solve these problems. The presentation of the proposed algorithm is complemented with examples that illustrate its effectiveness and manageable computing cost.

Automated summary

This algorithm computes nonrigid, possibly partial comparisons of shapes defined by unstructured triangulations of their surfaces. It finds a possibly partial correspondence between the vertices of two triangulations and measures the similarity of the shapes using a cost associated with this correspondence. The algorithm characterizes the vertices using signature vectors of features and computes the correspondence as the transport plan that solves the optimal transport or optimal partial transport problem between their sets of vertices using a statistical physics approach.