ConDA: Unsupervised Domain Adaptation for LiDAR Segmentation via Regularized Domain Concatenation

Lingdong Kong, Niamul Quader, Venice Erin Liong

Abstract: 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 an autonomous driving system. State-of-the-art methods in UDA often employ a key idea: utilizing joint supervision signals from both of the source domain (with ground-truth) and target domain (with pseudo-labels) for self-training. In this work, we improve and extend on 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 both 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 experiments, we demonstrate that ConDA significantly outperforms prior arts in mitigating the domain gap not only in UDA, but also in other DAs with minimum supervisions, such as semi-/weakly-supervised DAs.