论文标题
课程deepsdf
Curriculum DeepSDF
论文作者
论文摘要
当学习素描时，初学者从简单而灵活的形状开始，然后在随后的培训课程中逐渐争取更复杂和准确的训练。在本文中，我们设计了一个“形状课程”，用于在形状上学习连续签名的距离函数（SDF），即课程DeepSDF。受到人类学习方式的启发，Curriculum DeepSDF根据以下两个标准以困难的顺序组织学习任务：表面准确性和样本难度。前者认为对地面真理进行监督的严格性，而后者则将硬训练样品的权重接近复杂的几何形状和精细的结构。更具体地说，课程DeepSDF首先学会重建粗形，然后逐渐提高准确性，并更多地关注复杂的本地细节。实验结果表明，精心设计的课程通过相同的培训数据，训练时期和网络体系结构与DEEPSDF相同，从而可以更好地塑造重建。我们认为，形状课程的应用可以使各种3D形状表示方法学习方法的训练过程受益。
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.
