HYLDA: End-to-end Hybrid Learning Domain Adaptation for LiDAR Semantic Segmentation. (arXiv:2201.05585v1 [cs.CV])

In this paper we address the problem of training a LiDAR semantic
segmentation network using a fully-labeled source dataset and a target dataset
that only has a small number of labels. To this end, we develop a novel
image-to-image translation engine, and couple it with a LiDAR semantic
segmentation network, resulting in an integrated domain adaptation architecture
we call HYLDA. To train the system end-to-end, we adopt a diverse set of
learning paradigms, including 1) self-supervision on a simple auxiliary …

arxiv cv domain adaptation hybrid learning lidar segmentation semantic