Shape matching plays an important role in geometry processing and shape analysis. In the last decades, much research has been devoted to improve the quality of matching between surfaces. This huge effort is motivated by several applications such as object retrieval, animation and information transfer just to name a few. Shape matching is usually divided into two main categories: rigid and non rigid matching. In both cases, the standard evaluation is usually performed on shapes that share the same connectivity, in other words, shapes represented by the same mesh. This is mainly due to the availability of a “natural” ground truth that is given for these shapes. Indeed, in most cases the consistent connectivity directly induces a ground truth correspondence between vertices. However, this standard practice obviously does not allow to estimate the robustness of a method with respect to different connectivity. With this track, we propose a benchmark to evaluate the performance of point-to-p
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Hi-resolution three-dimensional nonrigid shapes in a variety of poses for non-rigid shape similarity and correspondence experiments. The database contains a total of 80 objects, including 11 cats, 9 dogs, 3 wolves, 8 horses, 6 centaurs, 4 gorillas, 12 female figures, and two different male figures, containing 7 and 20 poses. Typical vertex count is about 50,000. Objects within the same class have the same triangulation and an equal number of vertices numbered in a compatible way. This can be used as a per-vertex ground truth correspondence in correspondence experiments. Two representations are available: MATLAB file (.mat) and ASCII text files containing the 1-based list of triangular faces (.tri), and a list of vertex XYZ coordinates (.vert). A .png thumbnail is available for each object.
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