Pairwise point cloud registration is a critical task for many applications, which heavily depends on finding correct correspondences from the two point clouds. However, the low overlap between input point clouds causes the registration to fail easily, 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 pairwise registration and loop closure. Feature description is performed by a sparse UNet-like network based on BEV representation, and 3D keypoints are extracted by a detection head for 2D locations, and a regression head for heights. For overlap detection, a cross-attention module is applied for interacting contextual information of 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 estimation, especially in scenes with small overlaps. It also achieves top registration performance on both datasets in terms of translation and rotation errors.

翻译：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帧点云中找出正确的对应关系。然而，输入点云之间的低重叠度会导致配准容易失败，产生错误的匹配和误判的重叠区域，尤其是在非重叠区域包含相似结构的场景中。本文提出了一种统一的鸟瞰视图（BEV）模型，用于联合学习3D局部特征与重叠估计，以完成点云配准与回环检测。特征描述通过一个基于BEV表示的稀疏UNet-like网络实现，3D关键点提取则采用二维位置检测头与高度回归头。对于重叠检测，我们应用交叉注意力模块交互输入点云的上下文信息，并通过分类头估计重叠区域。我们在KITTI数据集和Apollo-SouthBay数据集上对统一模型进行了全面评估。实验表明，我们的方法在重叠估计方面显著优于现有方法，尤其是在低重叠场景下；同时在两个数据集的平移与旋转误差指标上均取得了最优的配准性能。