用深度图特征提升点云配准精度，无需训练数据。
RARE: Refine Any Registration of Pairwise Point Clouds via Zero-Shot Learning
- 通过多视角深度图提取扩散模型特征，融合几何信息
- 在多个数据集上显著提升配准准确率，优于基线方法
- 零样本设计，适合缺乏标注数据的场景
近期研究利用大规模预训练扩散模型，在图像中建立语义对应关系方面展现出潜力。受此启发，我们提出一种新颖的零样本点云配准精炼方法。该方法首先从多个视角将点云投影为深度图，并从预训练扩散网络中提取深度扩散特征，作为隐式知识。这些特征与现有方法获取的几何特征融合，以建立更精确的点云对应关系。基于优化后的对应关系，我们的方法显著提升了配准精度。大量实验表明，该方法不仅增强了现有配准技术的性能，还在多个数据集上表现出强泛化能力。代码已开源：https://github.com/zhengcy-lambo/RARE.git。
原文摘要 · Abstract (English)
Recent research leveraging large-scale pretrained diffusion models has demonstrated the potential of using diffusion features to establish semantic correspondences in images. Inspired by advancements in diffusion-based techniques, we propose a novel zero-shot method for refining point cloud registration algorithms. Our approach leverages correspondences derived from depth images to enhance point feature representations, eliminating the need for a dedicated training dataset. Specifically, we first project the point cloud into depth maps from multiple perspectives and extract implicit knowledge from a pretrained diffusion network as depth diffusion features. These features are then integrated with geometric features obtained from existing methods to establish more accurate correspondences between point clouds. By leveraging these refined correspondences, our approach achieves significantly improved registration accuracy. Extensive experiments demonstrate that our method not only enhances the performance of existing point cloud registration techniques but also exhibits robust generalization capabilities across diverse datasets. Codes are available at https://github.com/zhengcy-lambo/RARE.git.
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