Some deep learning-based point cloud registration methods struggle with zero-shot generalization, often requiring dataset-specific hyperparameter tuning or retraining for new environments. We identify three critical limitations: (a) fixed user-defined parameters (e.g., voxel size, search radius) that fail to generalize across varying scales, (b) learned keypoint detectors exhibit poor cross-domain transferability, and (c) absolute coordinates amplify scale mismatches between datasets. To address these three issues, we present BUFFER-X, a training-free registration framework that achieves zero-shot generalization through: (a) geometric bootstrapping for automatic hyperparameter estimation, (b) distribution-aware farthest point sampling to replace learned detectors, and (c) patch-level coordinate normalization to ensure scale consistency. Our approach employs hierarchical multi-scale matching to extract correspondences across local, middle, and global receptive fields, enabling robust registration in diverse environments. For efficiency-critical applications, we introduce BUFFER-X-Lite, which reduces total computation time by 43% (relative to BUFFER-X) through early exit strategies and fast pose solvers while preserving accuracy. We evaluate on a comprehensive benchmark comprising 12 datasets spanning object-scale, indoor, and outdoor scenes, including cross-sensor registration between heterogeneous LiDAR configurations. Results demonstrate that our approach generalizes effectively without manual tuning or prior knowledge of test domains. Code: https://github.com/MIT-SPARK/BUFFER-X.

翻译：部分基于深度学习的点云配准方法在零样本泛化方面存在局限，通常需要针对新环境进行数据集特定的超参数调整或重新训练。我们识别出三个关键缺陷：(a) 固定的用户定义参数（如体素尺寸、搜索半径）无法适应不同尺度场景；(b) 学习型关键点检测器在跨领域迁移中表现不佳；(c) 绝对坐标会放大数据集间的尺度失配。为解决这三个问题，我们提出BUFFER-X——一种免训练的配准框架，通过以下机制实现零样本泛化：(a) 几何自举法实现超参数自动估计；(b) 分布感知最远点采样替代学习型检测器；(c) 块级坐标归一化确保尺度一致性。该方法采用分层多尺度匹配机制，在局部、中层与全局感受野中提取对应关系，从而在多样化环境中实现鲁棒配准。针对计算效率敏感的应用场景，我们进一步提出BUFFER-X-Lite版本，通过提前退出策略与快速位姿求解器将总计算时间降低43%（相对于BUFFER-X），同时保持精度。我们在包含物体尺度、室内外场景的12个数据集构建的综合基准上进行评估，涵盖异构LiDAR配置间的跨传感器配准任务。实验结果表明，该方法无需人工调参或测试领域先验知识即可实现有效泛化。代码：https://github.com/MIT-SPARK/BUFFER-X。