通过匹配距离与几何分布提升多视角点云配准的可靠性
Matching Distance and Geometric Distribution Aided Learning Multiview Point Cloud Registration
- 利用点云对间的匹配距离设计网络，筛选可靠配对构建位姿图
- 基于几何分布信息，用神经网络端到端估计绝对位姿
- 适用于机器人、自动驾驶等需要高精度位姿同步的场景
多视角点云配准在机器人、自动化和计算机视觉中至关重要。本文聚焦于位姿图构建与运动同步问题。以往方法常通过全局特征聚合局部描述子构建稀疏图或剪枝全连接图，结果可能不可靠。为此，我们设计了一种网络模型，从点云对之间的匹配距离中提取信息，用于识别可靠的配对以构建位姿图。针对运动同步，提出另一种神经网络模型，以数据驱动方式直接计算绝对位姿，而非依赖不准确的手工设计损失函数。该模型融合几何分布信息，并采用改进的注意力机制，实现灵活且可靠的特征交互。在多个室内与室外数据集上的实验验证了方法的有效性与泛化能力。源代码见：https://github.com/Shi-Qi-Li/MDGD。
原文摘要 · Abstract (English)
Multiview point cloud registration plays a crucial role in robotics, automation, and computer vision fields. This paper concentrates on pose graph construction and motion synchronization within multiview registration. Previous methods for pose graph construction often pruned fully connected graphs or constructed sparse graph using global feature aggregated from local descriptors, which may not consistently yield reliable results. To identify dependable pairs for pose graph construction, we design a network model that extracts information from the matching distance between point cloud pairs. For motion synchronization, we propose another neural network model to calculate the absolute pose in a data-driven manner, rather than optimizing inaccurate handcrafted loss functions. Our model takes into account geometric distribution information and employs a modified attention mechanism to facilitate flexible and reliable feature interaction. Experimental results on diverse indoor and outdoor datasets confirm the effectiveness and generalizability of our approach. The source code is available at https://github.com/Shi-Qi-Li/MDGD.
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