  3D shape completion for real data is important but challenging, since partial
point clouds acquired by real-world sensors are usually sparse, noisy and
unaligned. Different from previous methods, we address the problem of learning
3D complete shape from unaligned and real-world partial point clouds. To this
end, we propose a weakly-supervised method to estimate both 3D canonical shape
and 6-DoF pose for alignment, given multiple partial observations associated
with the same instance. The network jointly optimizes canonical shapes and
poses with multi-view geometry constraints during training, and can infer the
complete shape given a single partial point cloud. Moreover, learned pose
estimation can facilitate partial point cloud registration. Experiments on both
synthetic and real data show that it is feasible and promising to learn 3D
shape completion through large-scale data without shape and pose supervision.
