Learning-based point cloud registration methods can handle clean point clouds well, while it is still challenging to generalize to noisy and partial point clouds. To this end, we propose a novel framework for noisy and partial point cloud registration. By introducing a neural implicit function representation, we replace the problem of rigid registration between point clouds with a registration problem between the point cloud and the neural implicit function. We then alternately optimize the implicit function representation and the registration between the implicit function and point cloud. In this way, point cloud registration can be performed in a coarse-to-fine manner. Since our method avoids computing point correspondences, it is robust to the noise and incompleteness of point clouds. Compared with the registration methods based on global features, our method can deal with surfaces with large density variations and achieve higher registration accuracy. Experimental results and comparisons demonstrate the effectiveness of the proposed framework.

翻译：基于学习的点云配准方法能够较好地处理干净点云，但在泛化至含噪声和不完整的点云时仍面临挑战。为此，我们提出一种针对噪声和不完整点云配准的新框架。通过引入神经隐式函数表示，我们将点云间的刚性配准问题转化为点云与神经隐式函数之间的配准问题，并交替优化隐式函数表示及其与点云间的配准。通过这种方式，可实现从粗到细的点云配准。由于本方法避免计算点对应关系，因此对点云的噪声和不完整性具有鲁棒性。与基于全局特征的配准方法相比，本方法能够处理存在大幅密度变化的曲面，并获得更高的配准精度。实验结果与对比分析验证了所提框架的有效性。