We study end-to-end learning strategies for 3D shape inference from images,
in particular from a single image. Several approaches in this direction have
been investigated that explore different shape representations and suitable
learning architectures. We focus instead on the underlying probabilistic
mechanisms involved and contribute a more principled probabilistic
inference-based reconstruction framework, which we coin Probabilistic
Reconstruction Networks. This framework expresses image conditioned 3D shape
inference through a family of latent variable models, and naturally decouples
the choice of shape representations from the inference itself. Moreover, it
suggests different options for the image conditioning and allows training in
two regimes, using either Monte Carlo or variational approximation of the
marginal likelihood. Using our Probabilistic Reconstruction Networks we obtain
single image 3D reconstruction results that set a new state of the art on the
ShapeNet dataset in terms of the intersection over union and earth mover's
distance evaluation metrics. Interestingly, we obtain these results using a
basic voxel grid representation, improving over recent work based on finer
point cloud or mesh based representations.

使用基于概率推理的重建框架，探索了基于不同形状表示和适当的学习架构的端到端学习策略，从而实现了从单个图像推断三维形状。通过我们的 Probabilistic Reconstruction Networks，使用基本的体素网格表征，实现了在 ShapeNet 数据集上的新的交集和移动距离评估指标的最新成果。