We present a framework for learning single-view shape and pose prediction
without using direct supervision for either. Our approach allows leveraging
multi-view observations from unknown poses as supervisory signal during
training. Our proposed training setup enforces geometric consistency between
the independently predicted shape and pose from two views of the same instance.
We consequently learn to predict shape in an emergent canonical (view-agnostic)
frame along with a corresponding pose predictor. We show empirical and
qualitative results using the ShapeNet dataset and observe encouragingly
competitive performance to previous techniques which rely on stronger forms of
supervision. We also demonstrate the applicability of our framework in a
realistic setting which is beyond the scope of existing techniques: using a
training dataset comprised of online product images where the underlying shape
and pose are unknown.

该篇论文提出一种无需直接监督的学习单视角形状和姿态预测的框架，通过利用未知姿态的多视角观察信号进行训练，并在训练中在同一实例的两个视图之间强制实施几何一致性来独立预测形状和姿态，从而学习在新出现的标准框架中预测形状和相应的姿态预测器。在使用 ShapeNet 数据集时，取得了与先前依赖更强监督形式的技术相当的鼓舞人心的竞争成绩。此外，还展示了该框架在现实环境中的适用性，该环境超出了现有技术范畴：使用由在线产品图像组成的训练数据集，在这些数据集内，底层的形状和姿态是未知的。