Source-Free Domain Adaptation (SFDA) aims to transfer a pre-trained source model to the unlabeled target domain without accessing the source data, thereby effectively solving labeled data dependency and domain shift problems. However, the SFDA setting faces a bottleneck due to the absence of supervisory information. To mitigate this problem, Active Learning (AL) is introduced to combine with SFDA, endeavoring to actively label a small set of the most high-quality target points so that models with satisfactory performance can be obtained at an acceptable cost. Nevertheless, several issues remain unresolved, namely when to query new labels during training, what kind of samples deserve labeling to ensure rich information, and where the labels should be distributed to guarantee diversity. Thus we elaborate OmniQuery to omnibearing address the “When, What, and Where” problems about active points querying in source-free domain adaptation for cross-modal 3D semantic segmentation. The method consists of three main components: Query Decider, Point Ranker, and Budget Slicer. The Query Decider determines the optimal timing to query new points by fitting the validation curves during training. The Point Ranker nominates points for annotation by calculating the ambiguity of neighboring points in the feature space. The Budget Slicer allocates the annotation quota, i.e., labeling percentage of the point cloud, to different semantic regions by utilizing the advanced 2D semantic segmentation capabilities of the Segment Anything Model (SAM). Extensive experiments demonstrate the effectiveness of our proposed method, achieving up to 99.64% of fully supervised performance with only 3% of labels, and consistently outperforming comparison methods across various scenarios.

Multi-modal test-time adaptation (TTA) for 3D semantic segmentation has increasingly become a research hotspot due to its ability to address label dependency and enable rapid adaptation. Existing methods rely on learnable extra components to mitigate reliability bias, however, learning-based approaches in TTA scenarios often lack sufficient training. Moreover, most existing approaches update only normalization layers in the teacher-student framework, which limits their ability to model domain shifts. To overcome these limitations, we propose PLATO-TTA, a novel multi-modal TTA method for 3D semantic segmentation leveraging the native stability in robust prototypes and adaptive tuning of critical teacher-student parameters. The approach contains three key components: Prototype-Guided Pseudo-Labeling (PGPL), Consistency Based Backtracking (CBB), and Domain Specific Updating (DSU). PGPL reduces reliability bias by constructing pseudo-source domain prototypes and computing modality fusion weights based on domain discrepancies. CBB updates all student model parameters while preventing catastrophic forgetting through a parameter backtracking mechanism. DSU selectively updates the teacher model using only domain-specific parameters from the student model, ensuring rapid adaptation and stable guidance. Extensive experiments demonstrate the effectiveness of PLATO-TTA, bringing a 6.3% gain to the SynthiatoSemanticKITTI scenario with severe reliability bias and significant domain discrepancy, and achieve state-of-the-art performance across various domain adaptation scenarios.