T-UDA: 時系列点群における時間的教師なしドメイン適応
T-UDA: Temporal Unsupervised Domain Adaptation in Sequential Point Clouds
時系列点群の時間的一貫性とセンサ間幾何一貫性を平均教師法と組み合わせ、ラベルなし走行シーンの3Dセマンティックセグメンテーションをドメイン適応する手法を提案。
著者: Awet Haileslassie Gebrehiwot, David Hurych, Karel Zimmermann, Patrick Pérez, Tomáš Svoboda
分類: cs.CV, cs.RO
原文アブストラクト
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.
