We address the problem of 3D shape completion from sparse and noisy point clouds, a fundamental problem in computer vision and robotics. Recent approaches are either data-driven or learning-based: Data-driven approaches rely on a shape model whose parameters are optimized to fit the observations; Learning-based approaches, in contrast, avoid the expensive optimization step by learning to directly predict complete shapes from incomplete observations in a fully-supervised setting. However, full supervision is often not available in practice. In this work, we propose a weakly-supervised learning-based approach to 3D shape completion which neither requires slow optimization nor direct supervision. While we also learn a shape prior on synthetic data, we amortize, i.e., learn, maximum likelihood fitting using deep neural networks resulting in efficient shape completion without sacrificing accuracy. On synthetic benchmarks based on ShapeNet and ModelNet as well as on real robotics data from KITTI and Kinect, we demonstrate that the proposed amortized maximum likelihood approach is able to compete with the fully supervised baseline of and outperforms the data-driven approach of, while requiring less supervision and being significantly faster.

本文提出了一种弱监督学习的三维形状补全方法，旨在解决从稀疏噪声点云进行三维形状补全的问题。与现有的数据驱动方法和基于学习的方法相比，该方法既不需要昂贵的优化步骤，也不需要直接监督，通过使用深度神经网络来学习最大似然拟合，具有高效和准确的形状补全能力。在来自ShapeNet、ModelNet、KITTI和Kinect的综合基准测试中，该方法能够与最近的完全监督基线相竞争，并且优于数据驱动方法，同时需要更少的监督并具有显着的速度优势。

弱监督下的三维形状补全学习