In this paper, we propose a novel 3D registration paradigm, Generative Point Cloud Registration, which bridges advanced 2D generative models with 3D matching tasks to enhance registration performance. Our key idea is to generate cross-view consistent image pairs that are well-aligned with the source and target point clouds, enabling geometry-color feature fusion to facilitate robust matching. To ensure high-quality matching, the generated image pair should feature both 2D-3D geometric consistency and cross-view texture consistency. To achieve this, we introduce Match-ControlNet, a matching-specific, controllable 2D generative model. Specifically, it leverages the depth-conditioned generation capability of ControlNet to produce images that are geometrically aligned with depth maps derived from point clouds, ensuring 2D-3D geometric consistency. Additionally, by incorporating a coupled conditional denoising scheme and coupled prompt guidance, Match-ControlNet further promotes cross-view feature interaction, guiding texture consistency generation. Our generative 3D registration paradigm is general and could be seamlessly integrated into various registration methods to enhance their performance. Extensive experiments on 3DMatch and ScanNet datasets verify the effectiveness of our approach.

翻译：本文提出了一种新颖的三维配准范式——生成式点云配准，该范式将先进的二维生成模型与三维匹配任务相结合，以提升配准性能。我们的核心思想是生成跨视角一致的图像对，使其与源点云和目标点云良好对齐，从而实现几何-颜色特征融合，以促进鲁棒匹配。为确保高质量的匹配，生成的图像对需同时具备二维-三维几何一致性与跨视角纹理一致性。为此，我们引入了Match-ControlNet，一种专为匹配设计的可控二维生成模型。具体而言，该模型利用ControlNet的深度条件生成能力，生成与点云衍生的深度图在几何上对齐的图像，从而确保二维-三维几何一致性。此外，通过结合耦合条件去噪方案与耦合提示引导，Match-ControlNet进一步促进了跨视角特征交互，引导纹理一致性生成。我们的生成式三维配准范式具有通用性，可无缝集成到多种配准方法中以提升其性能。在3DMatch和ScanNet数据集上的大量实验验证了本方法的有效性。