In this paper, we propose SRIF, a novel Semantic shape Registration framework based on diffusion-based Image morphing and Flow estimation. More concretely, given a pair of extrinsically aligned shapes, we first render them from multi-views, and then utilize an image interpolation framework based on diffusion models to generate sequences of intermediate images between them. The images are later fed into a dynamic 3D Gaussian splatting framework, with which we reconstruct and post-process for intermediate point clouds respecting the image morphing processing. In the end, tailored for the above, we propose a novel registration module to estimate continuous normalizing flow, which deforms source shape consistently towards the target, with intermediate point clouds as weak guidance. Our key insight is to leverage large vision models (LVMs) to associate shapes and therefore obtain much richer semantic information on the relationship between shapes than the ad-hoc feature extraction and alignment. As a consequence, SRIF achieves high-quality dense correspondences on challenging shape pairs, but also delivers smooth, semantically meaningful interpolation in between. Empirical evidence justifies the effectiveness and superiority of our method as well as specific design choices. The code is released at this https URL.
在本文中，我们提出了SRIF，一种基于扩散式图像变形和流估计的全新语义形状配准框架。具体而言，给定一对外部对齐的形状，我们首先从多个视角渲染它们，然后利用基于扩散模型的图像插值框架生成它们之间的中间图像序列。随后，这些图像会被输入到动态 3D 高斯散点框架中，我们通过该框架重建并后处理中间点云，以尊重图像变形过程。最终，我们为此设计了一种全新的配准模块，用于估计连续的归一化流，从而使源形状一致地变形为目标形状，并以中间点云作为弱引导。我们的关键见解是利用大规模视觉模型（LVMs）来关联形状，从而比依赖特定特征提取和对齐的方法获取更丰富的语义信息。因此，SRIF在具有挑战性的形状对上实现了高质量的密集对应，同时在形状之间提供了平滑且具有语义意义的插值。实验结果证明了我们方法的有效性和优越性，以及特定设计选择的合理性。