PLATO-TTA: Prototype-Guided Pseudo-Labeling and Adaptive Tuning for Multi-Modal Test-Time Adaptation of 3D Segmentation

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.