For relocalization in large-scale point clouds, we propose the first approach
that unifies global place recognition and local 6DoF pose refinement. To this
end, we design a Siamese network that jointly learns 3D local feature detection
and description directly from raw 3D points. It integrates FlexConv and
Squeeze-and-Excitation (SE) to assure that the learned local descriptor
captures multi-level geometric information and channel-wise relations. For
detecting 3D keypoints we predict the discriminativeness of the local
descriptors in an unsupervised manner. We generate the global descriptor by
directly aggregating the learned local descriptors with an effective attention
mechanism. In this way, local and global 3D descriptors are inferred in one
single forward pass. Experiments on various benchmarks demonstrate that our
method achieves competitive results for both global point cloud retrieval and
local point cloud registration in comparison to state-of-the-art approaches. To
validate the generalizability and robustness of our 3D keypoints, we
demonstrate that our method also performs favorably without fine-tuning on the
registration of point clouds that were generated by a visual SLAM system. Code
and related materials are available at
this https URL

本研究提出一种新方法，在大规模点云中实现位置重定位，通过使用 Siamese 网络同时学习全局场景识别和本地六度姿态细化算法来生成全球描述符，并通过有效的注意力机制聚合本地描述符，实现同时获得本地和全局 3D 描述符，实验表明，本方法在全球点云检索和本地点云对比方面比现有技术实现的结果更具竞争力。