T-UDA: Temporal Unsupervised Domain Adaptation in Sequential Point Clouds
Awet Haileslassie Gebrehiwot,David Hurych,Karel Zimmermann,Patrick Pérez,Tomáš Svoboda,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 traine...
