State-of-the-art 3D semantic segmentation models are trained on the off-the-shelf public benchmarks, but they often face the major challenge when these well-trained models are deployed to a new domain. In this paper, we propose an Active-and-Adaptive Segmentation (ADAS) baseline to enhance the weak cross-domain generalization ability of a well-trained 3D segmentation model, and bridge the point distribution gap between domains. Specifically, before the cross-domain adaptation stage begins, ADAS performs an active sampling operation to select a maximally-informative subset from both source and target domains for effective adaptation, reducing the adaptation difficulty under 3D scenarios. Benefiting from the rise of multi-modal 2D-3D datasets, ADAS utilizes a cross-modal attention-based feature fusion module that can extract a representative pair of image features and point features to achieve a bi-directional image-point feature interaction for better safe adaptation. Experimentally, ADAS is verified to be effective in many cross-domain settings including: 1) Unsupervised Domain Adaptation (UDA), which means that all samples from target domain are unlabeled; 2) Unsupervised Few-shot Domain Adaptation (UFDA) which means that only a few unlabeled samples are available in the unlabeled target domain; 3) Active Domain Adaptation (ADA) which means that the selected target samples by ADAS are manually annotated. Their results demonstrate that ADAS achieves a significant accuracy gain by easily coupling ADAS with self-training methods or off-the-shelf UDA works.

翻译：现有最先进的3D语义分割模型基于现成公开基准数据集训练，但当这些训练完备的模型部署至新领域时，常面临重大挑战。本文提出一种主—自适应分割（ADAS）基线，旨在增强训练完备的3D分割模型在跨域场景下的泛化能力，并弥合域间点分布差异。具体而言，在跨域适应阶段开始前，ADAS通过主动采样操作从源域和目标域中选取最具信息量的子集以进行高效适应，从而降低3D场景下的适应难度。得益于多模态2D-3D数据集的兴起，ADAS利用跨模态注意力特征融合模块，提取具有代表性的图像特征与点特征对，实现双向图像—点特征交互，以确保更安全的适应过程。实验表明，ADAS在多种跨域设定中均有效：1）无监督域适应（UDA），即目标域所有样本均无标注；2）无监督小样本域适应（UFDA），即无标注目标域中仅有少量未标注样本可用；3）主动域适应（ADA），即被ADAS选中的目标样本由人工标注。结果表明，将ADAS与自训练方法或现成UDA方法简单结合即可实现显著的精度提升。