Datasets › FPv1
FPv1
FPv1 (prior name FAUST-partial) is a 3D registration benchmark dataset created to address the lack of data variability in the existing 3D registration benchmarks such as: 3DMatch, ETH, KITTI.
The original FAUST training dataset is comprised of 100 3D scans of human bodies.
The benchmark generation for a single scan from the FAUST training dataset can be summarized as follows:
- Make xz-plane the floor by translating the minimal bounding box point of the scan to the origin
- Surround the scan with a regular icosahaedron. Each point of the icosahaedron acts as a viewpoint
- For each viewpoint, create a partial point cloud using the hidden point removal algorithm
Finally, for a pair of partial point clouds with the desired overalp, generate a random rotation from the desired rotation range and translation range.
Benchmarks archive 2025-07-28
All 1 leaderboard whose dataset resolves to this page shown (sort by any header). "First row" is the archive's own first row at snapshot, in the archive's row order; nothing here re-ranks and metric direction is not asserted.
| First row (archive order) | Paper | Code | ||||
|---|---|---|---|---|---|---|
| Point Cloud Registration | FPv1 | Greedy Grid Search Recall (3cm, 10 degrees) 92.81 | Challenging the Universal Representation of Deep Models... | davidboja/greedy-grid-search +1 | 8 | Compare |
Papers archive 2025-07-28
7 shown of 7 papers with a leaderboard row on this dataset's benchmarks, newest first. The archive's own "papers using this dataset" list was never published, so this is the benchmark-backed subset; the archive's count for this dataset is 7. The Syntology column is from Syntology's graph (read 2026-09-24), stated per sample; it is not part of any archive number.
| Date | Samples run Syntology | |||
|---|---|---|---|---|
| Challenging the Universal Representation of Deep Models for 3D Point Cloud Registration | 2 | 1 | 29 Nov 2022 | not harvested |
| Geometric Transformer for Fast and Robust Point Cloud Registration | 2 | 1 | 14 Feb 2022 | ran 4 of 9 samples (5 unverified; 9 pointer-only for licence) |
| You Only Hypothesize Once: Point Cloud Registration with Rotation-equivariant Descriptors | 1 | 2 | 1 Sep 2021 | not harvested |
| PointDSC: Robust Point Cloud Registration using Deep Spatial Consistency | 1 | 1 | 9 Mar 2021 | ran 2 of 3 samples (1 unverified; 3 pointer-only for licence) |
| SpinNet: Learning a General Surface Descriptor for 3D Point Cloud Registration | 1 | 1 | 24 Nov 2020 | ran 0 of 1 samples (1 unverified) |
| Distinctive 3D local deep descriptors | 2 | 1 | 1 Sep 2020 | not harvested |
| Fast Point Feature Histograms (FPFH) for 3D Registration | 1 | 1 | 12 May 2009 | not harvested |
Dataset loaders archive 2025-07-28
1 loader as listed in the archive; links are outbound and not re-checked here.
Tasks archive 2025-07-28
License archive 2025-07-28
No licence recorded in the archive. Absence here is not a statement about the dataset's terms.
Modalities archive 2025-07-28
Languages archive 2025-07-28
No language tagged.
Variants archive 2025-07-28
- FPv1
- FAUST-partial (60%+ overlap, Rot 0-45, Trans -50-50)
- FAUST-partial (60%+ overlap, Rot 0-45, Trans -50-50, trained on 3DMatch)
3 variant names, as the archive lists them.
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