BUFFER-X：迈向多样化场景下的零样本点云配准
BUFFER-X: Towards Zero-Shot Point Cloud Registration in Diverse Scenes
AI总结：
BUFFER-X通过自适应体素大小、最远点采样和补丁尺度归一化，实现多样化场景下的零样本点云配准，无需先验信息或手动调参。
摘要：
最近深度学习基于的点云配准技术在泛化能力上有所提升，但大多数方法仍需要为每个新环境重新训练或手动调整参数。在本文中，我们识别出限制泛化能力的三个关键因素：（a）依赖于环境特定的体素大小和搜索半径，（b）基于学习的关键点检测器在域外场景下的鲁棒性差，（c）原始坐标使用，这会加剧尺度差异。为了解决这些问题，我们提出了一种名为BUFFER-X的零样本配准流程，通过（a）自适应确定体素大小/搜索半径，（b）使用最远点采样绕过学习的检测器，（c）利用基于补丁的尺度归一化以保持坐标范围的一致性。特别是，我们提出了多尺度补丁基于的描述符生成和跨尺度的分层内点搜索，以提高在多样化场景中的鲁棒性。我们还提出了一种新的通用性基准，使用11个数据集涵盖各种室内/室外场景和传感器模态，证明了BUFFER-X在没有先验信息或手动参数调整的情况下，在测试数据集上实现了显著的泛化能力。我们的代码可在https://github.com/MIT-SPARK/BUFFER-X上获得。
英文摘要：
Recent advances in deep learning-based point cloud registration have improved generalization, yet most methods still require retraining or manual parameter tuning for each new environment. In this paper, we identify three key factors limiting generalization: (a) reliance on environment-specific voxel size and search radius, (b) poor out-of-domain robustness of learning-based keypoint detectors, and (c) raw coordinate usage, which exacerbates scale discrepancies. To address these issues, we present a zero-shot registration pipeline called BUFFER-X by (a) adaptively determining voxel size/search radii, (b) using farthest point sampling to bypass learned detectors, and (c) leveraging patch-wise scale normalization for consistent coordinate bounds. In particular, we present a multi-scale patch-based descriptor generation and a hierarchical inlier search across scales to improve robustness in diverse scenes. We also propose a novel generalizability benchmark using 11 datasets that cover various indoor/outdoor scenarios and sensor modalities, demonstrating that BUFFER-X achieves substantial generalization without prior information or manual parameter tuning for the test datasets. Our code is available at https://github.com/MIT-SPARK/BUFFER-X.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
