Adaptive-D3CTTA: Domain-Aware Prototype Learning and Temporal-Geometric Consistency for Continual Test-Time Adaptation of 3D LiDAR Semantic Segmentation

3D LiDAR semantic segmentation is a fundamental task in autonomous driving, but its performance can deteriorate when deployment conditions differ from those observed during training. Changes in point-cloud density, sensor characteristics, scene geometry, and object distributions create domain shifts that challenge models operating in continuously changing environments. This paper proposes Adaptive-D3CTTA, a domain-aware continual test-time adaptation framework for 3D LiDAR semantic segmentation. The framework combines range- and density-aware prototype learning, domain-conditioned feature decorrelation, temporal memory, K-nearest-neighbor (KNN) geometric consistency, and confidence-aware pseudo-label refinement. Range and density information are used to characterize incoming scans and support class-level feature representation, while temporal memory and local geometric agreement help assess the reliability of predictions across sequential observations. A confidence-based refinement mechanism selects reliable pseudo-labels for online adaptation. The framework is designed to process unlabeled target-domain streams without requiring target annotations during adaptation. Its performance should be evaluated using a clearly defined source-to-target protocol on SemanticKITTI and nuScenes-LiDARSeg, with comparisons against the source-only model and relevant test-time adaptation methods, including D³CTTA. Mean intersection-over-union, per-class intersection-over-union, mean class accuracy, adaptation stability, runtime, and memory consumption provide complementary measures of segmentation quality and deployment cost. The proposed framework aims to improve the reliability of continual adaptation by combining semantic, temporal, and geometric information within a unified pipeline. Its effectiveness must be established through reproducible experiments and component-wise ablation studies

3D LiDAR Semantic Segmentation, Continual Test-Time Adaptation, Domain Shift, Prototype Learning, Temporal Consistency, Geometric Consistency, Pseudo-Label Refinement, Autonomous Driving

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