When learning to sketch, beginners start with simple and flexible shapes, and
then gradually strive for more complex and accurate ones in the subsequent
training sessions. In this paper, we design a "shape curriculum" for learning
continuous Signed Distance Function (SDF) on shapes, namely Curriculum DeepSDF.
Inspired by how humans learn, Curriculum DeepSDF organizes the learning task in
ascending order of difficulty according to the following two criteria: surface
accuracy and sample difficulty. The former considers stringency in supervising
with ground truth, while the latter regards the weights of hard training
samples near complex geometry and fine structure. More specifically, Curriculum
DeepSDF learns to reconstruct coarse shapes at first, and then gradually
increases the accuracy and focuses more on complex local details. Experimental
results show that a carefully-designed curriculum leads to significantly better
shape reconstructions with the same training data, training epochs and network
architecture as DeepSDF. We believe that the application of shape curricula can
benefit the training process of a wide variety of 3D shape representation
learning methods.

本文设计了一个形状课程用于学习连续有符号距离函数（SDF）的形状，由简单到复杂地扩展其难度，帮助提升三维形状重建的质量和训练效果。