  Data plays a crucial role in training learning-based methods for 3D point
cloud registration. However, the real-world dataset is expensive to build,
while rendering-based synthetic data suffers from domain gaps. In this work, we
present PointRegGPT, boosting 3D point cloud registration using generative
point-cloud pairs for training. Given a single depth map, we first apply a
random camera motion to re-project it into a target depth map. Converting them
to point clouds gives a training pair. To enhance the data realism, we
formulate a generative model as a depth inpainting diffusion to process the
target depth map with the re-projected source depth map as the condition. Also,
we design a depth correction module to alleviate artifacts caused by point
penetration during the re-projection. To our knowledge, this is the first
generative approach that explores realistic data generation for indoor point
cloud registration. When equipped with our approach, several recent algorithms
can improve their performance significantly and achieve SOTA consistently on
two common benchmarks. The code and dataset will be released on
https://github.com/Chen-Suyi/PointRegGPT.
