Search Results for author:
Found 2 papers, 2 papers with code
T-UDA: Temporal Unsupervised Domain Adaptation in Sequential Point Clouds
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.
Teachers in concordance for pseudo-labeling of 3D sequential data
We propose to leverage sequences of point clouds to boost the pseudolabeling technique in a teacher-student setup via training multiple teachers, each with access to different temporal information.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
