Surface matching with salient keypoints in geodesic scale space

Abstract

This paper develops a new salient keypoints-based shape description which extracts the salient surface keypoints with detected scales. Salient geometric features can then be defined collectively on all the detected scale normalized local patches to form a shape descriptor for surface matching purpose. The saliency-driven keypoints are computed as local extrema of the difference of Gaussian function defined over a curved surface in geodesic scale space. This method can properly function on either manifold or non-manifold surface without resorting to any surface mapping or parameterization procedures. Therefore, it has a wide utility in many applications such as shape matching, classification, and recognition. Our experiments on 3D shapes demonstrate that the salient keypoints and local feature descriptors are robust and stable to noisy input and insensitive to resolution change. We have applied our technique to the tasks of 3D shape matching, and the experimental results showed good performance and the effectiveness of this new method.

3D point of interest detection via spectral irregularity diffusion

“…[24] selected points that contribute to improving retrieval performance by assigning a predicted distinctiveness value to each selected point using a training phase. [29] built to detect points of interest. [28] assumed that the vertices of a 3D object have associated information such as curvature or photometric properties.…”

Intrinsic Geometric Scale Space by Shape Diffusion

“…Causality For 3D objects, recent research in visual saliency suggests that features usually appear as curvature variance [15,36]. For example, a sphere or a plane produces little visual attractiveness as it has invariant curvature across the surface.…”

“…Feature scales are critical in a scale-space setup, since all subsequent local computations are conducted on this feature-adaptive support. In the scale spaces formulated via Gaussian smoothing [19,15,36], the scale of a detected keypoint is empirically defined as certain multiple of the standard deviation of the current Gaussian kernel. The support region is centered at the keypoint.…”

“…Having witnessed the success of scale space theory applied to image and image feature extraction [19,21], people are naturally inspired to generalize a similar framework for 3D shapes [36,8,26,23,16]. For instance, Kimmel constructed a geometric scale space for images painted on a given surface [13].…”

“…In [26] and [16], point clouds are explicitly smoothed by means of mean curvature flow and least squares projection, respectively, for multiscale shape representations. Zou et al [36] treated multiple geometric properties as an image retained on the surface, on which successive geodesic Gaussian smoothing is performed on the curved domain. A common characteristic of these methods is that, although labeled by different smoothing techniques, the original curved surfaces served as domains.…”