3D Unsupervised Domain Adaptation

Benchmarks archive 2025-07-28

No benchmark for this task in the archive.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

1 dataset whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

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Most implemented papers archive 2025-07-28

4 shown of 4 papers with code (4 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• PolarMix: A General Data Augmentation Technique for LiDAR Point Clouds
  30 Jul 2022 2 repositories listed Syntology ran 3 of 10 samples · 7 unverified
  The first is scene-level swapping which exchanges point cloud sectors of two LiDAR scans that are cut along the azimuth axis.
• CoSMix: Compositional Semantic Mix for Domain Adaptation in 3D LiDAR Segmentation
  20 Jul 2022 2 repositories listed
  We propose a new approach of sample mixing for point cloud UDA, namely Compositional Semantic Mix (CoSMix), the first UDA approach for point cloud segmentation based on sample mixing.
• SALUDA: Surface-based Automotive Lidar Unsupervised Domain Adaptation
  6 Apr 2023 1 repository listed
  Learning models on one labeled dataset that generalize well on another domain is a difficult task, as several shifts might happen between the data domains.
• Transfer Learning from Synthetic to Real LiDAR Point Cloud for Semantic Segmentation
  12 Jul 2021 1 repository listed
  Extensive experiments show that SynLiDAR provides a high-quality data source for studying 3D transfer and the proposed PCT achieves superior point cloud translation consistently across the three setups.

Syntology lines on 1 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.