针对机载激光点云分割的持续域适应，提升模型在动态环境中的稳定性与精度。
APCoTTA: Continual Test-Time Adaptation for Semantic Segmentation of Airborne LiDAR Point Clouds
- 选择性更新低置信度层，冻结稳定层以防止灾难性遗忘。
- 基于熵的自一致性损失，剔除不可靠样本，减少误差累积。
- 随机参数插值平衡新旧知识，适合长期部署的点云分割任务。
机载激光扫描（ALS）点云语义分割是大规模三维场景理解的基础任务。实际部署中，因环境与传感器变化导致的连续域偏移常使固定模型性能下降。连续测试时自适应（CTTA）可应对演化中的无标签域，但其在ALS点云上的应用仍不充分，受限于缺乏基准数据集及灾难性遗忘与误差累积风险。为此，我们提出专用于ALS点云分割的新型CTTA框架APCoTTA。该框架包含三个核心组件：首先，将梯度驱动的层选择机制适配至ALS点云，仅更新低置信度层并冻结稳定层，以保留源知识并缓解遗忘；其次，引入基于熵的一致性损失，剔除不可靠样本，并仅对可靠样本施加一致性正则化，有效降低误差累积，提升适应稳定性；第三，采用随机参数插值机制，随机混合适应后参数与源模型参数，进一步平衡目标域适应与源知识保留。最后，我们构建了两个新基准ISPRSC和H3DC，填补了ALS点云分割中CTTA基准的空白。大量实验表明，APCoTTA在两个基准上均表现优异，相较于直接推理，mIoU分别提升约9%和14%。相关代码与数据集已开源：https://github.com/Gaoyuan2/APCoTTA。
原文摘要 · Abstract (English)
Airborne laser scanning (ALS) point cloud semantic segmentation is a fundamental task for large-scale 3D scene understanding. Fixed models deployed in real-world scenarios often suffer from performance degradation due to continuous domain shifts caused by environmental and sensor changes. Continuous Test-Time Adaptation (CTTA) enables adaptation to evolving unlabeled domains, but its application to ALS point clouds remains underexplored, hindered by the lack of benchmarks and the risks of catastrophic forgetting and error accumulation. To address these challenges, we propose APCoTTA (ALS Point cloud Continuous Test-Time Adaptation), a novel CTTA framework tailored for ALS point cloud semantic segmentation. APCoTTA consists of three key components. First, we adapt a gradient-driven layer selection mechanism for ALS point clouds, selectively updating low-confidence layers while freezing stable ones to preserve source knowledge and mitigate catastrophic forgetting. Second, an entropy-based consistency loss discards unreliable samples and enforces consistency regularization solely on reliable ones, effectively reducing error accumulation and improving adaptation stability. Third, a random parameter interpolation mechanism stochastically blends adapted parameters with source model parameters, further balancing target adaptation and source knowledge retention. Finally, we construct two benchmarks, ISPRSC and H3DC, to address the lack of CTTA benchmarks for ALS point cloud segmentation. Extensive experiments demonstrate that APCoTTA achieves superior performance on both benchmarks, improving mIoU by approximately 9\% and 14\% over direct inference. The new benchmarks and code are available at https://github.com/Gaoyuan2/APCoTTA.
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