  Real scans always miss partial geometries of objects due to the
self-occlusions, external-occlusions, and limited sensor resolutions. Point
cloud completion aims to refer the complete shapes for incomplete 3D scans of
objects. Current deep learning-based approaches rely on large-scale complete
shapes in the training process, which are usually obtained from synthetic
datasets. It is not applicable for real-world scans due to the domain gap. In
this paper, we propose a self-supervised point cloud completion method (TraPCC)
for vehicles in real traffic scenes without any complete data. Based on the
symmetry and similarity of vehicles, we make use of consecutive point cloud
frames to construct vehicle memory bank as reference. We design a bottom-up
mechanism to focus on both local geometry details and global shape features of
inputs. In addition, we design a scene-graph in the network to pay attention to
the missing parts by the aid of neighboring vehicles. Experiments show that
TraPCC achieve good performance for real-scan completion on KITTI and nuScenes
traffic datasets even without any complete data in training. We also show a
downstream application of 3D detection, which benefits from our completion
approach.
