针对激光雷达语义分割在真实场景中性能下降的问题，提出观测约束的测试时微调方法。
No Adaptation Without Observation: Observability-Constrained Test-Time Prompt Tuning for LiDAR Semantic Segmentation
- 基于深度一致性与邻域支持估计每点感知可靠性，动态加权空间监督信号。
- 仅在轻量级提示适配器中更新参数，并通过空间门控避免不可靠区域干扰全局表示。
- 适用于车载激光雷达部署中无标注数据下的稳定在线适应，尤其适合边缘设备。
激光雷达语义分割在真实部署中常因感知条件变化而性能下降，但重新标注数据不现实。测试时自适应（TTA）通过伪标签在线更新模型参数，但直接应用于激光雷达数据存在挑战：由于距离相关的稀疏性和遮挡导致伪标签可靠性空间异质，对全局共享参数进行统一更新会引入不稳定的梯度并破坏适应过程。本文提出一种几何约束的测试时提示微调框架。该方法从深度一致的光束终止点和邻域支持估计每位置的感知可靠性，并据此重加权空间监督信号。适应仅作用于冻结主干网络中的轻量级提示适配器，结合空间门控机制，防止不可靠区域扰动全局表示。同时采用时间平滑的原型对齐策略，通过积累可靠语义证据提升在线更新稳定性。在标准激光雷达基准测试上，该方法在无额外标注条件下显著提升了适应稳定性和分割性能。
原文摘要 · Abstract (English)
LiDAR semantic segmentation often degrades under real-world deployment due to evolving sensing conditions, while collecting new annotations for retraining is impractical. Test-time adaptation (TTA) updates model parameters online using pseudo-label supervision, but directly applying standard TTA strategies to LiDAR data is challenging. Because pseudo-label reliability is spatially heteroscedastic under range-dependent sparsity and occlusion, uniform updates on globally shared parameters can inject unstable gradients and destabilize adaptation. We propose a geometry-constrained test-time prompt tuning framework for LiDAR semantic segmentation. Our method estimates per-location sensing reliability from depth-consistent beam terminations and neighborhood support, and uses it to reweight spatial supervision. Adaptation is confined to lightweight prompt adapters inserted into a frozen backbone, with spatial gating to prevent unreliable regions from perturbing globally shared representations. A temporally smoothed prototype alignment strategy further stabilizes online updates by accumulating reliable semantic evidence over time. Experiments on standard LiDAR benchmarks demonstrate improved adaptation stability and segmentation performance under deployment variations without additional annotations.
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