We present a novel 3D shape reconstruction method which learns to predict an
implicit 3D shape representation from a single RGB image. Our approach uses a
set of single-view images of multiple object categories without viewpoint
annotation, forcing the model to learn across multiple object categories
without 3D supervision. To facilitate learning with such minimal supervision,
we use category labels to guide shape learning with a novel categorical metric
learning approach. We also utilize adversarial and viewpoint regularization
techniques to further disentangle the effects of viewpoint and shape. We obtain
the first results for large-scale (more than 50 categories) single-viewpoint
shape prediction using a single model without any 3D cues. We are also the
first to examine and quantify the benefit of class information in single-view
supervised 3D shape reconstruction. Our method achieves superior performance
over state-of-the-art methods on ShapeNet-13, ShapeNet-55 and Pascal3D+.

本文提出了一种基于单个 RGB 图像学习预测隐式 3D 物体形状表征的新型 3D 形状重构方法，使用多种无视角注释的单视角图像进行跨类别学习且仅使用标签进行监督，辅以分类度量学习与对抗性和视点正则化技术，取得了 ShapeNet-13，ShapeNet-55 和 Pascal3D + 数据集上优于最先进方法的效果。