Intrinsic Shape Matching by Planned Landmark Sampling

Abstract

Recently, the problem of intrinsic shape matching has received a lot of attention. A number of algorithms have been proposed, among which random-sampling-based techniques have been particularly successful due to their generality and efficiency. We introduce a new sampling-based shape matching algorithm that uses a planning step to find optimized "landmark" points. These points are matched first in order to maximize the information gained and thus minimize the sampling costs. Our approach makes three main contributions: First, the new technique leads to a significant improvement in performance, which we demonstrate on a number of benchmark scenarios. Second, our technique does not require any keypoint detection. This is often a significant limitation for models that do not show sufficient surface features. Third, we examine the actual numerical degrees of freedom of the matching problem for a given piece of geometry. In contrast to previous results, our estimates take into account unprecise geodesics and potentially numerically unfavorable geometry of general topology, giving a more realistic complexity estimate.