{"url":"/sota/point-cloud-quality-assessment-on-m-pccd","task":{"name":"Point Cloud Quality Assessment","url":"/task/point-cloud-quality-assessment","note":null},"dataset":{"name":"M-PCCD","url":"/dataset/m-pccd"},"category":null,"categories":[],"category_note":null,"description":"### Background\r\nA large and dense collection of points in three-dimensional space, collected by sensors such as LiDAR, is known as a point cloud. Points in the point cloud consist of geometric properties, such as three-dimensional spatial coordinates (x, y, z), and other attributes like color, reflectance, opacity, etc., represented by feature vectors. Since point clouds can directly represent the 3D world, they are widely employed in various fields, such as photogrammetry, power monitoring, architectural surveying, digital manufacturing, autonomous driving, gaming, cultural heritage \r\n reservation, and more. \r\n### Significance\r\nInteractive point clouds typically contain millions of colored points and may possess complex attributes. To address the substantial transmission bandwidth and storage space required by point clouds, \r\n esearchers have developed various point cloud compression (PCC) techniques. However, point cloud compression may introduce significant visual distortions. In addition, deformations and distortions frequently occur during the acquisition, processing, transmission, rendering, and interaction of point clouds, all of which degrade the visual quality of the point cloud, ultimately impacting the application’s user experience. Therefore, effective methods\r\nfor quantifying the quality of compressed point clouds are needed. More generally, point cloud quality assessment (PCQA) is crucial for optimizing and evaluating point cloud processing algorithms, such as encoding, denoising, and super-resolution.","description_from":"task","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","rank":"the archive's row order at snapshot; not re-ranked","rows_end_at":"2025-07-28","rows_withheld_as_spam":0,"metric_values":"the archive's strings, untouched"},"metrics":["Pearson Correlation Coefficient "],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"Pearson Correlation Coefficient ":"higher"}},"counts":{"rows":1,"rows_with_code":1,"rows_with_paper_page":1,"rows_dated":1,"rows_using_additional_data":0},"rows":[{"rank_in_archive_order":1,"model":"-","metrics":{"Pearson Correlation Coefficient ":"95.6"},"uses_additional_data":false,"paper_date":"2021-08-05","paper":"/paper/joint-geometry-and-color-projection-based","paper_url":"https://arxiv.org/abs/2108.02481v1","paper_title":"Joint Geometry and Color Projection-based Point Cloud Quality Metric","code":"https://github.com/AlirezaJav/Projection-based-PC-Quality-Metric","n_code_links":1,"syntology":null}],"since_archive":{"claim":"Results that newer papers report for their own method, placed here by Syntology. A model pointed at the cell in the paper's own table; the number was read from that cell and checked against this leaderboard's metric, dataset, split and scale; an independent check that saw this leaderboard's other rows and every other leaderboard on the same dataset accepted it. Not reviewed by the paper's authors or by the archive's editors, and not ranked against the archive rows.","extraction_file_present":true,"measurement":{"test_papers":883,"papers_with_output":881,"judged_true":108,"judged":110,"wilson95_lower":0.9361,"measured_on":"2026-09-24","frozen_commit":"0e3de0df94"},"measurement_note":"blind adjudication of accepted entries on a held-out split of archive papers, rules frozen before the test","coverage":{"sentence":"Syntology has checked 6,885 of the 9,623 papers on this site that are newer than the archive; results from the others appear after they are checked.","complete":false,"papers_newer_than_archive":9623,"papers_checked":6885,"papers_extracted_not_yet_verified":0,"boards_without_verdict":2,"papers_not_yet_extracted":2737},"order":"newest first by month (arXiv date, else the arXiv-id month), then arXiv id descending","columns":[],"entries":[]},"syntology":{"read_at":"2026-09-25T09:33:49+00:00","claim":"Per row: N of M harvested code samples from that row's paper executed on a synthesized fixture; the other M-N are unverified. Not a reproduction of the row's number; not a correctness claim. n_pointer_only_licence counts samples the site points at rather than redistributes (a licence axis, independent of ran/unverified).","rows_with_graph_line":0,"rows_with_any_sample_ran":0,"distinct_papers_with_graph_line":0,"distinct_papers_with_any_sample_ran":0,"samples_over_distinct_papers":{"n_ran":0,"n_unverified":0,"n_samples":0,"n_pointer_only_licence":0,"note":"each paper (arXiv id) counted once, however many rows it is behind; this is the page-level figure"},"samples_row_weighted":{"n_ran":0,"n_unverified":0,"n_samples":0,"n_pointer_only_licence":0,"note":"row-weighted: a paper behind several rows is counted once per row; inflated relative to samples_over_distinct_papers by design, kept for readers summing the per-row syntology blocks"}}}