T-UDA: Temporal Unsupervised Domain Adaptation in Sequential Point
  Clouds
Awet Haileslassie Gebrehiwot David Hurych Karel Zimmermann Patrick Pérez Tomáš Svoboda
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 https://github.com/ctu-vras/T-UDA.