Papers › PCN: Point Completion Network
PCN: Point Completion Network
Wentao Yuan, Tejas Khot, David Held, Christoph Mertz, Martial Hebert
Shape completion, the problem of estimating the complete geometry of objects from partial observations, lies at the core of many vision and robotics applications. In this work, we propose Point Completion Network (PCN), a novel learning-based approach for shape completion. Unlike existing shape completion methods, PCN directly operates on raw point clouds without any structural assumption (e.g. symmetry) or annotation (e.g. semantic class) about the underlying shape. It features a decoder design that enables the generation of fine-grained completions while maintaining a small number of parameters. Our experiments show that PCN produces dense, complete point clouds with realistic structures in the missing regions on inputs with various levels of incompleteness and noise, including cars from LiDAR scans in the KITTI dataset.
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Code
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Code Syntology ran Syntology
10 samples harvested; 1 ran; 0 honoured the contract we drafted; 9 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Point Cloud Completion | Completion3D | PCN | Chamfer Distance | 18.22(?) | #2 of 7 | Archive leaderboard | report |
| Point Cloud Completion | ShapeNet | PCN | Chamfer Distance | 9.636 | #7 of 11 | Archive leaderboard | report |
| Point Cloud Completion | ShapeNet | PCN | Chamfer Distance L2 | 4.016 | #7 of 11 | Archive leaderboard | report |
| Point Cloud Completion | ShapeNet | PCN | F-Score@1% | 0.695 | #7 of 11 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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