论文标题
PULSAR：有效的基于球体的神经渲染
Pulsar: Efficient Sphere-based Neural Rendering
论文作者
论文摘要
我们提出了PULSAR，这是一种有效的基于球体的可区分渲染器，它的数量级比竞争技术，模块化和易于使用的数量级，这是由于其与Pytorch的紧密整合。可区分的渲染是现代神经渲染方法的基础，因为它可以通过图像观察对3D场景表示的端到端培训。但是，基于梯度的神经网格，体素或功能表示的优化面临着多种挑战，即拓扑不一致，高内存足迹或缓慢的渲染速度。为了减轻这些问题，PULSAR采用：1）基于球体的场景表示，2）有效的可区分渲染引擎和3）神经阴影。 PULSAR执行比现有技术快的数量级，并允许对数百万球的代表进行实时渲染和优化。使用球形作为场景表示，在避免拓扑问题的同时，获得了前所未有的速度。 Pulsar是完全可区分的，因此可以实现大量应用，从3D重建到一般的神经渲染。
We propose Pulsar, an efficient sphere-based differentiable renderer that is orders of magnitude faster than competing techniques, modular, and easy-to-use due to its tight integration with PyTorch. Differentiable rendering is the foundation for modern neural rendering approaches, since it enables end-to-end training of 3D scene representations from image observations. However, gradient-based optimization of neural mesh, voxel, or function representations suffers from multiple challenges, i.e., topological inconsistencies, high memory footprints, or slow rendering speeds. To alleviate these problems, Pulsar employs: 1) a sphere-based scene representation, 2) an efficient differentiable rendering engine, and 3) neural shading. Pulsar executes orders of magnitude faster than existing techniques and allows real-time rendering and optimization of representations with millions of spheres. Using spheres for the scene representation, unprecedented speed is obtained while avoiding topology problems. Pulsar is fully differentiable and thus enables a plethora of applications, ranging from 3D reconstruction to general neural rendering.
