  Transferring knowledge learned from the labeled source domain to the raw
target domain for unsupervised domain adaptation (UDA) is essential to the
scalable deployment of autonomous driving systems. State-of-the-art methods in
UDA often employ a key idea: utilizing joint supervision signals from both
source and target domains for self-training. In this work, we improve and
extend this aspect. We present ConDA, a concatenation-based domain adaptation
framework for LiDAR segmentation that: 1) constructs an intermediate domain
consisting of fine-grained interchange signals from both source and target
domains without destabilizing the semantic coherency of objects and background
around the ego-vehicle; and 2) utilizes the intermediate domain for
self-training. To improve the network training on the source domain and
self-training on the intermediate domain, we propose an anti-aliasing
regularizer and an entropy aggregator to reduce the negative effect caused by
the aliasing artifacts and noisy pseudo labels. Through extensive studies, we
demonstrate that ConDA significantly outperforms prior arts in mitigating
domain gaps.
