LiDAR-UDA: Self-ensembling Through Time for Unsupervised LiDAR Domain
  Adaptation
Amirreza Shaban JoonHo Lee Sanghun Jung Xiangyun Meng Byron Boots
We introduce LiDAR-UDA, a novel two-stage self-training-based Unsupervised
Domain Adaptation (UDA) method for LiDAR segmentation. Existing self-training
methods use a model trained on labeled source data to generate pseudo labels
for target data and refine the predictions via fine-tuning the network on the
pseudo labels. These methods suffer from domain shifts caused by different
LiDAR sensor configurations in the source and target domains. We propose two
techniques to reduce sensor discrepancy and improve pseudo label quality: 1)
LiDAR beam subsampling, which simulates different LiDAR scanning patterns by
randomly dropping beams; 2) cross-frame ensembling, which exploits temporal
consistency of consecutive frames to generate more reliable pseudo labels. Our
method is simple, generalizable, and does not incur any extra inference cost.
We evaluate our method on several public LiDAR datasets and show that it
outperforms the state-of-the-art methods by more than $3.9\%$ mIoU on average
for all scenarios. Code will be available at
https://github.com/JHLee0513/LiDARUDA.