Omkar2118/datasets huggingface dataset:
SHREC'19
Full name: SHREC'19 track Matching Humans with Different Connectivity

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-point matching pipelines when the shapes to be matched have different connectivity (see Figure 1). We consider the concurrent presence of 1) different meshing, 2) rigid transformation in 3D space, 3) non-rigid deformations, 4) different vertex density, ranging from 5K to more than 50K, and 5) topological changes induced by mesh gluing in areas of contact. The correspondence between these shapes is obtained through the recently proposed registration pipeline FARM [1]. This method provides a high-quality registration of the SMPL model [2] to a large set of human meshes coming from different datasets from which we obtain a well-defined correspondence for all the meshes registered and SMPL itself.

Introducing paper: FARM: Functional Automatic Registration Method for 3D Human Bodies
TOSCA
Full name: TOSCA high-resolution

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.
SHREC'16 Partial Benchmark
Full name: SHREC 2016 TRACK: PARTIAL MATCHING OF DEFORMABLE SHAPES

Finding a correspondence between two shapes is a fundamental task in computer graphics and geometry processing with applications ranging from texture mapping to animation. A particularly challenging and widely studied setting is when shapes are allowed to undergo quasi-isometric deformations, as it happens when we consider articulated bodies in different poses. An even more interesting scenario is partial correspondence, where one is shown only a subset of the shape, and has to match it with a deformable version thereof. Partial correspondence problems arise in numerous applications that involve real data acquisition by 3D sensors, which inevitably leads to missing parts due to occlusions and partial views.

We propose a benchmark to evaluate the performance of algorithms for establishing correspondences between a full shape and its deformed versions, under the presence of different amounts and kinds of partiality.