EADReg：基于高效自回归扩散模型的概率对应生成用于室外点云配准
EADReg: Probabilistic Correspondence Generation with Efficient Autoregressive Diffusion Model for Outdoor Point Cloud Registration
AI总结：
本文提出EADReg框架，通过双向高斯混合模型粗配准和自回归扩散模型精配准，在保证推理效率的同时实现室外LiDAR点云的鲁棒配准，并在KITTI和NuScenes上取得最优性能。
摘要：
扩散模型在点云配准（PCR）任务中展现出巨大潜力，尤其是在增强对挑战性场景的鲁棒性方面。然而，现有的基于扩散的PCR方法主要关注实例级场景，难以处理室外LiDAR点云，因为LiDAR点固有的稀疏性、不规则性和庞大规模对建立稠密的全局点对点对应关系构成挑战。为解决这一问题，我们提出了一种名为EADReg的新框架，基于自回归扩散模型实现LiDAR点云的高效且鲁棒的配准。EADReg遵循由粗到精的配准范式。在粗配准阶段，我们采用双向高斯混合模型（BGMM）来剔除离群点并获得净化后的点云对。BGMM在源帧和目标帧的高斯混合模型（GMM）之间建立对应关系，从而基于过滤后的特征和几何信息实现可靠的粗配准。在精配准阶段，我们将基于扩散的PCR视为自回归过程来生成鲁棒的点对应关系，并在上层进行迭代优化。尽管基于扩散的方法常因推理速度受到批评，EADReg的运行时间可与基于卷积的方法相媲美。在KITTI和NuScenes基准数据集上的大量实验证明了我们所提方法的最先进性能。代码将在发表后发布。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
