  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. The code is available at
https://github.com/wuqianliang/Diff-Reg.
