论文标题
深活动的表面模型
Deep Active Surface Models
论文作者
论文摘要
活动的表面模型具有悠久的历史，可用于建模复杂的3D表面，但仅与深层网络结合使用了主动轮廓，然后仅用于产生数据术语以及控制它们的元参数映射。在本文中，我们主张一个更严格的整合。我们介绍了实现它们的层，可以将它们无缝集成到图形卷积网络中，以以可接受的计算成本来实现复杂的平滑度先验。我们将表明，所得的深活动表面模型优于使用传统正规化损失项的相同体系结构，以从2D图像和3D体积分割施加平滑度先验。
Active Surface Models have a long history of being useful to model complex 3D surfaces but only Active Contours have been used in conjunction with deep networks, and then only to produce the data term as well as meta-parameter maps controlling them. In this paper, we advocate a much tighter integration. We introduce layers that implement them that can be integrated seamlessly into Graph Convolutional Networks to enforce sophisticated smoothness priors at an acceptable computational cost. We will show that the resulting Deep Active Surface Models outperform equivalent architectures that use traditional regularization loss terms to impose smoothness priors for 3D surface reconstruction from 2D images and for 3D volume segmentation.
