Abstract
Estimation of the shape dissimilarity between 3D models is a very important problem in both computer vision and graphics for 3D surface reconstruction, modeling, matching, and compression. In this paper, we propose a novel method called surface roving technique to estimate the shape dissimilarity between 3D models. Unlike conventional methods, our surface roving approach exploits a virtual camera and Z-buffer, which is commonly used in 3D graphics. The corresponding points on different 3D models can be easily identified, and also the distance between them is determined efficiently, regardless of the representation types of the 3D models. Moreover, by employing the viewpoint sampling technique, the overall computation can be greatly reduced so that the dissimilarity is obtained rapidly without loss of accuracy. Experimental results show that the proposed algorithm achieves fast and accurate measurement of shape dissimilarity for different types of 3D object models.
Highlights
In 3D computer vision and graphics, shape recovery and modeling have been one of the major research fields during the last few years
We present the experimental results of measuring the shape dissimilarity using the proposed cooperative surface roving method
Viewpoint sampling replaces the surface roving if the test point is visible from a sample viewpoint in its normal orthographic direction
Summary
In 3D computer vision and graphics, shape recovery and modeling have been one of the major research fields during the last few years. Multiresolution surface representation, object recognition, and 3D data compression, it is essential to estimate the geometric distortion or shape dissimilarity in object space, since the performance of an algorithm cannot be evaluated quantitatively without it. Most existing methods use vertex-to-vertex [8, 14], vertexto-plane [3, 9], point-to-surface [2, 5, 15], and surface-tosurface distances [4, 6, 7, 11]. A direct implementation of it is not trivial, since it usually requires a brute force search which is impractical when the surface model is complex. Point-to-surface and surfaceto-surface distances provide more exact measurements, while finding the corresponding point set is still a problem. In most literature, by imposing additional assumptions and constraints, the brute force search is replaced by a local search
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