Deep perception models have to reliably cope with an open-world setting of
domain shifts induced by different geographic regions, sensor properties,
mounting positions, and several other reasons. Since covering all domains with
annotated data is technically intractable due to the endless possible
variations, researchers focus on unsupervised domain adaptation (UDA) methods
that adapt models trained on one (source) domain with annotations available to
another (target) domain for which only unannotated data are available. Current
predominant methods either leverage semi-supervised approaches, e.g.,
teacher-student setup, or exploit privileged data, such as other sensor
modalities or temporal data consistency. We introduce a novel domain adaptation
method that leverages the best of both trends. Our approach combines input
data's temporal and cross-sensor geometric consistency with the mean teacher
method. Dubbed T-UDA for "temporal UDA", such a combination yields massive
performance gains for the task of 3D semantic segmentation of driving scenes.
Experiments are conducted on Waymo Open Dataset, nuScenes and SemanticKITTI,
for two popular 3D point cloud architectures, Cylinder3D and MinkowskiNet. Our
codes are publicly available at this https URL

通过结合输入数据的时间和跨传感器的几何一致性和均值教师方法，我们引入了一种新颖的领域适应方法，称为 T-UDA（时间 UDA），该方法在驾驶场景的 3D 语义分割任务中取得了显著的性能提升。