针对腹腔镜手术点云配准中的真实数据偏差，提出测试时自适应方法提升配准精度。
Test Time Adaptation Methods for Point Cloud Registration in Laparoscopic Surgery
- 改进三种主流测试时自适应方法以应对术中点云的非对称噪声与缺失
- 输入级自适应在合成与真实数据上均显著降低配准误差，性能最优
- 计算开销小，适合临床实时应用，尤其适用于术中点云质量差场景
腹腔镜手术中的3D点云配准需估计术中器官重建点云与术前网格间的变换。由于真实数据无真值标签，监督网络在合成器官对上训练。但测试时的真实重建点云存在噪声、稀疏和遮挡，导致对应关系估计性能下降。测试时自适应（TTA）可缓解域偏移问题，但现有方法多依赖分类任务中的置信度、熵或类别原型，不适用于配准任务。配准涉及成对输入且存在非对称偏移，主要影响术中点云。本文分析并改造三类先进TTA方法：模型更新、无需反向传播的令牌过滤、特征对齐与层归一化校准，使其适配非对称点云场景，并替换分类导向的熵目标。基于在干净合成数据上训练的对应关系模型，在P2P和P2ILReg数据集上评估对污染合成与真实目标数据的适应效果。合成目标数据施加8种畸变（如均匀噪声、全局密度降低），共5个严重等级。所有方法在P2P上均提升配准性能；而在P2ILReg上，仅输入适应有效降错，归一化适应反而恶化结果。综合计算开销考量，输入适应最具实用性，实现低延迟推理与跨数据集稳定性能提升。
原文摘要 · Abstract (English)
3D point cloud registration in laparoscopic surgery estimates the transformation between an intraoperative organ reconstructed from video and its preoperative mesh. Because ground-truth transformations are unavailable for real data, supervised networks are trained on synthetic organ pairs. At test time, real reconstructions differ from synthetic data and are noisy, sparse, and occluded, which degrades correspondence estimation. Test-time adaptation (TTA) can reduce this domain shift, but existing methods mainly rely on logits, entropy, class prototypes, or cache memories unavailable in registration. Registration also involves paired inputs with an asymmetric shift that primarily affects the intraoperative cloud. We analyse and modify state-of-the-art TTA methods from three families to 3D registration: model, normalization, and input adaptation. We analyze four representative approaches based on auxiliary-task model updates, backpropagation-free token purging, feature alignment, and layer-normalization calibration. We modify them to handle asymmetric shifts between preoperative and intraoperative point clouds and replace classification-based entropy objectives. Using a correspondence-based model trained on clean synthetic source data, we evaluate adaptation to corrupted synthetic and real target data on P2P and P2ILReg. For synthetic targets, we apply eight corruptions, including uniform noise and global density reduction, at five severity levels. All methods improve registration on P2P, whereas on P2ILReg only input adaptation reduces the error, while normalization adaptation degrades it. Considering the computational overhead of backpropagation-based adaptation, input adaptation is the most promising option for laparoscopic surgery, providing low inference latency and consistent error reductions across datasets. Code: https://github.com/ninaa-git/survey_pc_registration_tta
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