  3D object detection is crucial for applications like autonomous driving and
robotics. However, in real-world environments, variations in sensor data
distribution due to sensor upgrades, weather changes, and geographic
differences can adversely affect detection performance. Semi-Supervised Domain
Adaptation (SSDA) aims to mitigate these challenges by transferring knowledge
from a source domain, abundant in labeled data, to a target domain where labels
are scarce. This paper presents a new SSDA method referred to as
Target-Oriented Domain Augmentation (TODA) specifically tailored for
LiDAR-based 3D object detection. TODA efficiently utilizes all available data,
including labeled data in the source domain, and both labeled data and
unlabeled data in the target domain to enhance domain adaptation performance.
TODA consists of two stages: TargetMix and AdvMix. TargetMix employs mixing
augmentation accounting for LiDAR sensor characteristics to facilitate feature
alignment between the source-domain and target-domain. AdvMix applies
point-wise adversarial augmentation with mixing augmentation, which perturbs
the unlabeled data to align the features within both labeled and unlabeled data
in the target domain. Our experiments conducted on the challenging domain
adaptation tasks demonstrate that TODA outperforms existing domain adaptation
techniques designed for 3D object detection by significant margins. The code is
available at: https://github.com/rasd3/TODA.
