Establishing reliable correspondences is essential for registration tasks
such as 3D and 2D3D registration. Existing methods commonly leverage geometric
or semantic point features to generate potential correspondences. However,
these features may face challenges such as large deformation, scale
inconsistency, and ambiguous matching problems (e.g., symmetry). Additionally,
many previous methods, which rely on single-pass prediction, may struggle with
local minima in complex scenarios. To mitigate these challenges, we introduce a
diffusion matching model for robust correspondence construction. Our approach
treats correspondence estimation as a denoising diffusion process within the
doubly stochastic matrix space, which gradually denoises (refines) a doubly
stochastic matching matrix to the ground-truth one for high-quality
correspondence estimation. It involves a forward diffusion process that
gradually introduces Gaussian noise into the ground truth matching matrix and a
reverse denoising process that iteratively refines the noisy matching matrix.
In particular, the feature extraction from the backbone occurs only once during
the inference phase. Our lightweight denoising module utilizes the same feature
at each reverse sampling step. Evaluation of our method on both 3D and 2D3D
registration tasks confirms its effectiveness.

建立可靠的对应关系对于 3D 和 2D3D 对准等任务至关重要。为了解决大变形、尺度不一致和模糊匹配问题所带来的挑战，本文引入了一种用于建立鲁棒对应关系的扩散匹配模型，在双随机矩阵空间内将对应估计视为去噪扩散过程，逐步去噪（细化）双随机匹配矩阵至准确的匹配矩阵，以进行高质量的对应估计。该方法在 3D 和 2D3D 对准任务上的评估验证了其有效性。