NeRF-Casting：通过一致的反射改进视角相关外观

摘要

神经辐射场（NeRFs）通常难以重建和渲染高度反光的物体，其外观随视角变化迅速而变化。最近的研究改进了NeRF渲染远处环境光照的详细反光外观的能力，但无法合成较近内容的一致反射。此外，这些技术依赖于大型计算昂贵的神经网络来建模出射辐射，严重限制了优化和渲染速度。我们提出了一种基于光线追踪的方法来解决这些问题：我们的模型不是查询昂贵的神经网络以获取沿着每个摄像机光线的点的出射视角相关辐射，而是从这些点投射反射光线，并通过NeRF表示跟踪这些光线，以渲染特征向量，然后使用一个小型廉价网络将其解码为颜色。我们证明了我们的模型在合成包含有光亮物体的场景的视图合成方面优于先前的方法，并且是唯一能够在真实场景中合成逼真的反光外观和反射的现有NeRF方法，同时需要与当前最先进的视图合成模型相当的优化时间。

English

Neural Radiance Fields (NeRFs) typically struggle to reconstruct and render highly specular objects, whose appearance varies quickly with changes in viewpoint. Recent works have improved NeRF's ability to render detailed specular appearance of distant environment illumination, but are unable to synthesize consistent reflections of closer content. Moreover, these techniques rely on large computationally-expensive neural networks to model outgoing radiance, which severely limits optimization and rendering speed. We address these issues with an approach based on ray tracing: instead of querying an expensive neural network for the outgoing view-dependent radiance at points along each camera ray, our model casts reflection rays from these points and traces them through the NeRF representation to render feature vectors which are decoded into color using a small inexpensive network. We demonstrate that our model outperforms prior methods for view synthesis of scenes containing shiny objects, and that it is the only existing NeRF method that can synthesize photorealistic specular appearance and reflections in real-world scenes, while requiring comparable optimization time to current state-of-the-art view synthesis models.

NeRF-Casting：通过一致的反射改进视角相关外观

NeRF-Casting: Improved View-Dependent Appearance with Consistent Reflections

摘要

Summary

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