{"url":"/dataset/shrec-19","name":"SHREC'19","full_name":"SHREC'19 track Matching Humans with Different Connectivity","description_markdown":"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.","description_withheld":null,"homepage":"http://profs.scienze.univr.it/~marin/shrec19/","introduced_date":"2018-07-27","introduced_date_note":null,"introduced_by":{"paper":"/paper/farm-functional-automatic-registration-method","title":"FARM: Functional Automatic Registration Method for 3D Human Bodies","first_author":"Riccardo Marin","url":null},"license":null,"modalities":[{"name":"3D","url":"/datasets/modality/3d"},{"name":"Point cloud","url":"/datasets/modality/point-cloud"},{"name":"3d meshes","url":"/datasets/modality/3d-meshes"}],"tasks":[{"name":"3D Dense Shape Correspondence","url":"/task/3d-dense-shape-correspondence","datasets_with_task":"/datasets/task/3d-dense-shape-correspondence"}],"languages":[],"variants":["SHREC'19"],"data_loaders":[],"num_papers_in_archive":20,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/3d-dense-shape-correspondence-on-shrec-19","task":"3D Dense Shape Correspondence","dataset_variant":"SHREC'19","rows":11,"metrics":["Euclidean Mean Error (EME)","Accuracy at 1%"],"first_row_in_archive_order":{"model":"Diffusion 3D Features (Zero-shot)","paper":"/paper/diffusion-3d-features-diff3f-decorating","metrics":{"Accuracy at 1%":"26.4","Euclidean Mean Error (EME)":"1.7"},"code_links":[{"title":"niladridutt/Diffusion-3D-Features","url":"https://github.com/niladridutt/Diffusion-3D-Features"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/unsupervised-template-assisted-point-cloud","title":"Unsupervised Template-assisted Point Cloud Shape Correspondence Network","date":"2024-03-25","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/diffusion-3d-features-diff3f-decorating","title":"Diffusion 3D Features (Diff3F): Decorating Untextured Shapes with Distilled Semantic Features","date":"2023-11-28","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":1,"samples_unverified":0,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/se-ornet-self-ensembling-orientation-aware","title":"SE-ORNet: Self-Ensembling Orientation-aware Network for Unsupervised Point Cloud Shape Correspondence","date":"2023-04-10","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/dpc-unsupervised-deep-point-correspondence","title":"DPC: Unsupervised Deep Point Correspondence via Cross and Self Construction","date":"2021-10-16","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/corrnet3d-unsupervised-end-to-end-learning-of","title":"CorrNet3D: Unsupervised End-to-end Learning of Dense Correspondence for 3D Point Clouds","date":"2020-12-31","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/correspondence-learning-via-linearly","title":"Correspondence Learning via Linearly-invariant Embedding","date":"2020-10-25","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/learning-elementary-structures-for-3d-shape","title":"Learning elementary structures for 3D shape generation and matching","date":"2019-08-13","rows_on_this_dataset":1,"code_links":3,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":0,"samples_unverified":2,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/3d-coded-3d-correspondences-by-deep-1","title":"3D-CODED : 3D Correspondences by Deep Deformation","date":"2018-06-13","rows_on_this_dataset":1,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":2,"samples_harvested":3,"samples_ran":1,"samples_unverified":2,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":1,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}