论文标题
野外弱监督的3D形状完成
Weakly-supervised 3D Shape Completion in the Wild
论文作者
论文摘要
实际数据的3D形状完成是重要的，但具有挑战性，因为现实世界传感器获得的部分点云通常稀疏，嘈杂且不对齐。与以前的方法不同，我们解决了学习3D完整形状的问题，从未对齐和现实世界的部分点云中。为此，我们提出了一种弱监督的方法，以估计与同一实例相关的多个部分观测值，以估计3D规范形状和6DOF姿势以进行比对。网络在训练过程中共同优化了具有多视图几何约束的规范形状和摆姿势，并可以在单个部分点云下推断完整的形状。此外，学到的姿势估计可以促进部分点云注册。合成和真实数据的实验表明，可以通过大规模数据完成3D形状完成，而没有形状和姿势监督。
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.
