ADAS: A Simple Active-and-Adaptive Baseline for Cross-Domain 3D Semantic Segmentation

• URL: http://arxiv.org/abs/2212.10390v2
• Date: Wed, 21 Dec 2022 12:47:03 GMT
• Title: ADAS: A Simple Active-and-Adaptive Baseline for Cross-Domain 3D Semantic Segmentation
• Authors: Ben Fei, Siyuan Huang, Jiakang Yuan, Botian Shi, Bo Zhang, Tao Chen, Min Dou, Yu Qiao
• Abstract summary: We propose an Active-and-Adaptive (ADAS) baseline to enhance the weak cross-domain generalization ability of a well-trained 3D segmentation model. ADAS performs an active sampling operation to select a maximally-informative subset from both source and target domains for effective adaptation. 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.
• Score: 38.66509154973051
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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