  3D point cloud completion is very challenging because it heavily relies on
the accurate understanding of the complex 3D shapes (e.g., high-curvature,
concave/convex, and hollowed-out 3D shapes) and the unknown & diverse patterns
of the partially available point clouds. In this paper, we propose a novel
solution,i.e., Point-block Carving (PC), for completing the complex 3D point
cloud completion. Given the partial point cloud as the guidance, we carve a3D
block that contains the uniformly distributed 3D points, yielding the entire
point cloud. To achieve PC, we propose a new network architecture, i.e.,
CarveNet. This network conducts the exclusive convolution on each point of the
block, where the convolutional kernels are trained on the 3D shape data.
CarveNet determines which point should be carved, for effectively recovering
the details of the complete shapes. Furthermore, we propose a sensor-aware
method for data augmentation,i.e., SensorAug, for training CarveNet on richer
patterns of partial point clouds, thus enhancing the completion power of the
network. The extensive evaluations on the ShapeNet and KITTI datasets
demonstrate the generality of our approach on the partial point clouds with
diverse patterns. On these datasets, CarveNet successfully outperforms the
state-of-the-art methods.
