  As the development of 3D sensors, registration of 3D data (e.g. point cloud)
coming from different kind of sensor is dispensable and shows great demanding.
However, point cloud registration between different sensors is challenging
because of the variant of density, missing data, different viewpoint, noise and
outliers, and geometric transformation. In this paper, we propose a method to
learn a 3D descriptor for finding the correspondent relations between these
challenging point clouds. To train the deep learning framework, we use
synthetic 3D point cloud as input. Starting from synthetic dataset, we use
region-based sampling method to select reasonable, large and diverse training
samples from synthetic samples. Then, we use data augmentation to extend our
network be robust to rotation transformation. We focus our work on more general
cases that point clouds coming from different sensors, named cross-source point
cloud. The experiments show that our descriptor is not only able to generalize
to new scenes, but also generalize to different sensors. The results
demonstrate that the proposed method successfully aligns two 3D cross-source
point clouds which outperforms state-of-the-art method.
