Unsupervised Domain Adaptation (UDA) technique has been explored in 3D
cross-domain tasks recently. Though preliminary progress has been made, the
performance gap between the UDA-based 3D model and the supervised one trained
with fully annotated target domain is still large. This motivates us to
consider selecting partial-yet-important target data and labeling them at a
minimum cost, to achieve a good trade-off between high performance and low
annotation cost. To this end, we propose a Bi-domain active learning approach,
namely Bi3D, to solve the cross-domain 3D object detection task. The Bi3D first
develops a domainness-aware source sampling strategy, which identifies
target-domain-like samples from the source domain to avoid the model being
interfered by irrelevant source data. Then a diversity-based target sampling
strategy is developed, which selects the most informative subset of target
domain to improve the model adaptability to the target domain using as little
annotation budget as possible. Experiments are conducted on typical
cross-domain adaptation scenarios including cross-LiDAR-beam, cross-country,
and cross-sensor, where Bi3D achieves a promising target-domain detection
accuracy (89.63% on KITTI) compared with UDAbased work (84.29%), even
surpassing the detector trained on the full set of the labeled target domain
(88.98%). Our code is available at: this https URL

提出了一种基于 Bi-domain active learning 的方法 Bi3D，通过 source 和 target 两个域的样本选择策略来解决跨域 3D 目标检测任务，实验结果表明该方法在不同的跨域情况下达到了较好的检测准确率。