3D Unsupervised Domain Adaptation

3 papers with code • 1 benchmarks • 1 datasets

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Most implemented papers

CoSMix: Compositional Semantic Mix for Domain Adaptation in 3D LiDAR Segmentation

saltoricristiano/cosmix-uda 20 Jul 2022

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

xiaoaoran/polarmix 30 Jul 2022

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

xiaoaoran/SynLiDAR 12 Jul 2021

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.