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M-PCCD Benchmark (Point Cloud Quality Assessment)
Background
A 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 reservation, and more.
Significance
Interactive 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, 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 for 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.
The archive carries no text for this table; the description above is the archive's text for the task Point Cloud Quality Assessment. archive 2025-07-28
Over time archive 2025-07-28
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Direction inferred from the metric name, not from the archive: Pearson Correlation Coefficient (higher is better). Points are placed at the row's paper date; 1 of 1 rows carry one.
Results archive 2025-07-28
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All 1 rows shown. 1 link to a paper page on this site; 0 are marked as using additional training data in the archive. No GitHub stars are tracked; "Code" is the first repository the archive lists for the row. The archive carries no row tags, review links or community-submitted rows for this table; none are shown. archive 2025-07-28
Syntology Ran reads "N of M ran · U unverified": of the M code samples Syntology harvested from repositories linked to that row's paper (joined by arXiv id), N executed on a synthesized input and the other U = M−N are unverified (harvested, no recorded run). It counts code from repositories linked to that row's paper, not this result: the row's number was not reproduced and nothing here is a correctness claim. The other cell texts mean no graph line for the row: "linked, not harvested" (the archive links code, Syntology has not harvested it), "no code linked" (no code link in the archive), "not matched" (the row's paper URL matched no paper on this site). 0 rows have a graph line, from 0 distinct papers; 0 rows (0 papers) have at least one sample that ran. Counting each paper once: Syntology ran 0 of 0 samples; 0 unverified. Separately, 0 of those 0 are pointer-only (licence): the site points at that code rather than redistributing it, a licence property recorded for ran and unverified samples alike; each cell's tooltip carries the row's own pointer-only count. Read from the graph 2026-09-24. Per-sample status is on the paper page.
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