3D Unsupervised Domain Adaptation
4 papers with code • 3 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in 3D Unsupervised Domain Adaptation
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SynLiDAR
Most implemented papers
CoSMix: Compositional Semantic Mix for Domain Adaptation in 3D LiDAR Segmentation
saltoricristiano/cosmix-uda
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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.
PolarMix: A General Data Augmentation Technique for LiDAR Point Clouds
The first is scene-level swapping which exchanges point cloud sectors of two LiDAR scans that are cut along the azimuth axis.
Transfer Learning from Synthetic to Real LiDAR Point Cloud for Semantic Segmentation
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.
SALUDA: Surface-based Automotive Lidar Unsupervised Domain Adaptation
valeoai/saluda
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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.
