We propose a novel 3d shape representation for 3d shape reconstruction from a single image. Rather than predicting a shape directly, we train a network to generate a training set which will be fed into another learning algorithm to define the shape. The nested optimization problem can be modeled by bi-level optimization. Specifically, the algorithms for bi-level optimization are also being used in meta learning approaches for few-shot learning. Our framework establishes a link between 3D shape analysis and few-shot learning. We combine training data generating networks with bi-level optimization algorithms to obtain a complete framework for which all components can be jointly trained. We improve upon recent work on standard benchmarks for 3d shape reconstruction.

翻译：我们从一个图像中为三维形状重建提出一个新的三维形状代表。 我们不是直接预测一个形状,而是训练一个网络来生成一个训练组,这些训练组将输入另一个用于定义形状的学习算法。 嵌套优化问题可以通过双级优化模型来模拟。 具体地说,双级优化的算法也用于元学习方法, 用于微小的学习。 我们的框架在三维形状分析与几光学习之间建立了联系。 我们把培训数据生成网络与双级优化算法结合起来, 以获得一个完整的框架, 所有组成部分都可以联合培训。 我们最近改进了三维形状重建的标准基准。