Diffusion models have shown the great potential in the point cloud registration (PCR) task, especially for enhancing the robustness to challenging cases. However, existing diffusion-based PCR methods primarily focus on instance-level scenarios and struggle with outdoor LiDAR points, where the sparsity, irregularity, and huge point scale inherent in LiDAR points pose challenges to establishing dense global point-to-point correspondences. To address this issue, we propose a novel framework named EADReg for efficient and robust registration of LiDAR point clouds based on autoregressive diffusion models. EADReg follows a coarse-to-fine registration paradigm. In the coarse stage, we employ a Bi-directional Gaussian Mixture Model (BGMM) to reject outlier points and obtain purified point cloud pairs. BGMM establishes correspondences between the Gaussian Mixture Models (GMMs) from the source and target frames, enabling reliable coarse registration based on filtered features and geometric information. In the fine stage, we treat diffusion-based PCR as an autoregressive process to generate robust point correspondences, which are then iteratively refined on upper layers. Despite common criticisms of diffusion-based methods regarding inference speed, EADReg achieves runtime comparable to convolutional-based methods. Extensive experiments on the KITTI and NuScenes benchmark datasets highlight the state-of-the-art performance of our proposed method. Codes will be released upon publication.

本研究解决了户外LiDAR点云配准中由于稀疏性和不规则性所带来的挑战。我们提出了EADReg框架，结合双向高斯混合模型和自回归扩散模型，实现了高效且稳健的配准。实验证明，EADReg在KITTI和NuScenes数据集上表现出色，具备与卷积基方法相当的推理速度。

EADReg：基于高效自回归扩散模型的户外点云配准概率对应生成