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.

本研究利用弱监督的方法，通过多视角几何约束来同时估计3D的标准形状和6自由度姿态，解决了学习未对准和真实世界部分点云的情况下，利用大量数据进行3D形状补全的问题。实验证明, 学习姿势估计可以促进部分点云的配准。

野外场景弱监督3D形状补全