Unsupervised domain adaptation (UDA) addresses the problem of distribution shift between the unlabelled target domain and labelled source domain. While the single target domain adaptation (STDA) is well studied in the literature for both 2D and 3D vision tasks, multi-target domain adaptation (MTDA) is barely explored for 3D data despite its wide real-world applications such as autonomous driving systems for various geographical and climatic conditions. We establish an MTDA baseline for 3D point cloud data by proposing to mix the feature representations from all domains together to achieve better domain adaptation performance by an ensemble average, which we call Mixup Ensemble Average or MEnsA. With the mixed representation, we use a domain classifier to improve at distinguishing the feature representations of source domain from those of target domains in a shared latent space. In empirical validations on the challenging PointDA-10 dataset, we showcase a clear benefit of our simple method over previous unsupervised STDA and MTDA methods by large margins (up to 17.10% and 4.76% on averaged over all domain shifts).

翻译：无监督域自适应（UDA）旨在解决无标签目标域与有标签源域之间的分布偏移问题。尽管单目标域自适应（STDA）在2D和3D视觉任务中已有充分研究，但多目标域自适应（MTDA）在自动驾驶系统等跨地理与气候条件的实际应用中广泛存在，却鲜少针对3D数据进行探索。本文通过提出对所有域的混合特征表示进行集成平均的方法——称为混合集成平均（Mix-up Ensemble Average，简称MEnsA）——为3D点云数据建立了MTDA基线。利用混合表示，我们引入域分类器以提升共享隐空间中区分源域与目标域特征表示的能力。在具有挑战性的PointDA-10数据集上的实证验证表明，我们提出的简单方法相较于此前无监督STDA及MTDA方法取得了显著优势（在所有域偏移上的平均性能提升最高达17.10%和4.76%）。