Local Weighted Dissimilarity Measure Based Multiscale 3D Keypoint Detection

This paper proposes a multiscale 3D keypoint detection method based on local weighted dissimilarity measure. At first, compute the local weighted dissimilarity measure of each vertex at different scale. Then determine the detecting scale of each vertex. Finally compare the local weighted dissimilarity measure of each vertex with those of its neighboring surface points at its detecting scale. The keypoint is defined as the vertex that has highest local weighted dissimilarity measure in its neighborhood. The contribution of this paper includes that we propose a novel local weighted dissimilarity measure and the frame of multiscale keypoint detection method. The proposed local weighted dissimilarity measure is computed from the shape index value, and it is invariable to rotation and translation transformation. The multiscale algorithm frame enable the detected key points are robust to noise, especially to high level noise. Extensive experiments have performed to testify the effectiveness of the proposed method.