论文标题
整洁：神经自适应断层扫描
NeAT: Neural Adaptive Tomography
论文作者
论文摘要
在本文中，我们介绍了神经适应性层析成像（整洁），这是用于多视图逆渲染的第一个自适应，分层神经渲染管道。通过神经特征与自适应显式表示的结合，我们实现了重建时间，远远超过了现有的神经反向渲染方法。自适应的显式表示通过促进空间中的空空间和集中样品来提高效率，而神经特征则充当3D重建的神经正常化程序。整洁的框架专为层析成像设置而设计，该设置仅由半透明的体积场景而不是不透明的对象组成。在这种情况下，整洁的表现要优于现有基于优化的层析成像求解器的质量，同时又要快速更快。
In this paper, we present Neural Adaptive Tomography (NeAT), the first adaptive, hierarchical neural rendering pipeline for multi-view inverse rendering. Through a combination of neural features with an adaptive explicit representation, we achieve reconstruction times far superior to existing neural inverse rendering methods. The adaptive explicit representation improves efficiency by facilitating empty space culling and concentrating samples in complex regions, while the neural features act as a neural regularizer for the 3D reconstruction. The NeAT framework is designed specifically for the tomographic setting, which consists only of semi-transparent volumetric scenes instead of opaque objects. In this setting, NeAT outperforms the quality of existing optimization-based tomography solvers while being substantially faster.
