Xiong Fengguang, Kong Yu, Xie Shuaikang, Kuang Liqun, Han Xie
Shanxi Provincial Key Laboratory of Machine Vision and Virtual Reality, Taiyuan, 030051, China.
School of Computer Science and Technology, North University of China, Taiyuan, 030051, China.
Sci Rep. 2024 Mar 6;14(1):5560. doi: 10.1038/s41598-024-56217-9.
Deformable attention only focuses on a small group of key sample-points around the reference point and make itself be able to capture dynamically the local features of input feature map without considering the size of the feature map. Its introduction into point cloud registration will be quicker and easier to extract local geometric features from point cloud than attention. Therefore, we propose a point cloud registration method based on Spatial Deformable Transformer (SDT). SDT consists of a deformable self-attention module and a cross-attention module where the deformable self-attention module is used to enhance local geometric feature representation and the cross-attention module is employed to enhance feature discriminative capability of spatial correspondences. The experimental results show that compared to state-of-the-art registration methods, SDT has a better matching recall, inlier ratio, and registration recall on 3DMatch and 3DLoMatch scene, and has a better generalization ability and time efficiency on ModelNet40 and ModelLoNet40 scene.
可变形注意力仅聚焦于参考点周围的一小群关键采样点，并使其能够动态捕捉输入特征图的局部特征，而无需考虑特征图的大小。将其引入点云配准中，相比于注意力，能够更快且更容易地从点云中提取局部几何特征。因此，我们提出了一种基于空间可变形Transformer（SDT）的点云配准方法。SDT由一个可变形自注意力模块和一个交叉注意力模块组成，其中可变形自注意力模块用于增强局部几何特征表示，交叉注意力模块用于增强空间对应关系的特征判别能力。实验结果表明，与现有最先进的配准方法相比，SDT在3DMatch和3DLoMatch场景上具有更好的匹配召回率、内点率和配准召回率，并且在ModelNet40和ModelLoNet40场景上具有更好的泛化能力和时间效率。
