Scale space based feature point detection on surfaces

Abstract

The detection of stable feature points is an important preprocessing step for many applications in computer graphics. Especially, registration and matching often require feature points and depend heavily on their quality. In the 2D image case, scale space based feature detection is well established and shows unquestionably good results. We introduce a novel scale space generalization to 3D embedded surfaces for extracting surface features. In contrast to a straightforward generalization to 3D images our approach extracts intrinsic features. We argue that such features are superior, in particular in the context of partial matching. Our features are robust to noise and provide a good description of the object’s salient regions.