Pairwise point cloud registration is a critical task for many applications, which heavily depends on finding the right correspondences from the two point clouds. However, the low overlap between the input point clouds makes the registration prone to fail, leading to mistaken overlapping and mismatched correspondences, especially in scenes where non-overlapping regions contain similar structures. In this paper, we present a unified bird's-eye view (BEV) model for jointly learning of 3D local features and overlap estimation to fulfill the pairwise registration and loop closure. Feature description based on BEV representation is performed by a sparse UNet-like network, and the 3D keypoints are extracted by a detection head for 2D locations and a regression head for heights, respectively. For overlap detection, a cross-attention module is applied for interacting contextual information of the input point clouds, followed by a classification head to estimate the overlapping region. We evaluate our unified model extensively on the KITTI dataset and Apollo-SouthBay dataset. The experiments demonstrate that our method significantly outperforms existing methods on overlap prediction, especially in scenes with small overlaps. The registration precision also achieves top performance on both datasets in terms of translation and rotation errors. Source codes will be available soon.

翻译：成对点云配准是许多应用中的关键任务，其效果高度依赖于从两个点云中找出正确对应关系。然而，输入点云之间的低重叠度会导致配准容易失败，尤其在非重叠区域包含相似结构的场景中，容易产生错误的重叠判断和误匹配对应关系。本文提出一种统一的鸟瞰视角（BEV）模型，用于联合学习三维局部特征与重叠估计，以实现成对配准和闭环检测。基于BEV表示的特征描述通过稀疏UNet类网络执行，三维关键点分别由二维位置检测头和高度回归头提取。在重叠检测方面，应用交叉注意力模块交互输入点云的上下文信息，随后通过分类头估计重叠区域。我们在KITTI数据集和Apollo-SouthBay数据集上对该统一模型进行了全面评估。实验表明，我们的方法在重叠预测方面显著优于现有方法，尤其在低重叠场景中。在平移误差和旋转误差指标上，本方法的配准精度在两个数据集中均达到最优性能。源代码将稍后公开。