SHREC19

Homepage

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.

UTILITY Although recent publications in the field of shape analysis achieve high-quality results in point-to-point matching, their quantitative evaluations have been performed only on meshes that share the connectivity (e.g. the widely used FAUST dataset [3]). The unique quantitative evaluation of the robustness to different density and distribution of the vertices is performed remeshing independently the shape as done in [4]. Otherwise, this robustness is usually not evaluated. This lack suggests that the community needs a new benchmark where robustness to this kind of nuisance can be assessed. We believe that by constructing a new, large, specific and challenging dataset of human bodies discretized with different density and mesh connectivity, we will provide a valuable testbed and hopefully foster further interest of the community in this more realistic scenario.