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.

我们提出了一个新的基于神经隐式函数表达的框架，用于处理噪声和不完整点云的注册问题，并通过一个分阶段的优化方式，实现了点云的精确匹配，避免了点对应计算，提升了鲁棒性和精度。