We present SOLID-Net, a neural network for spatially-varying outdoor lighting
estimation from a single outdoor image for any 2D pixel location. Previous work
has used a unified sky environment map to represent outdoor lighting. Instead,
we generate spatially-varying local lighting environment maps by combining
global sky environment map with warped image information according to geometric
information estimated from intrinsics. As no outdoor dataset with image and
local lighting ground truth is readily available, we introduce the SOLID-Img
dataset with physically-based rendered images and their corresponding intrinsic
and lighting information. We train a deep neural network to regress intrinsic
cues with physically-based constraints and use them to conduct global and local
lightings estimation. Experiments on both synthetic and real datasets show that
SOLID-Net significantly outperforms previous methods.

该研究介绍了一种名为 SOLID-Net 的神经网络用于从单个室外图像估计不同位置的室外光照。该方法使用一个全局天空环境图和图像中的几何信息，生成了可变的局部光照环境图，利用其进行了全局和局部的光照估计，并且利用全球物理上合理的数据集，证明其性能优于之前的方法。

从内部参数估计空间变化的室外照明

Spatially-Varying Outdoor Lighting Estimation from Intrinsics

We present a deep learning approach to reconstruct scene appearance from
unstructured images captured under collocated point lighting. At the heart of
Deep Reflectance Volumes is a novel volumetric scene representation consisting
of opacity, surface normal and reflectance voxel grids. We present a novel
physically-based differentiable volume ray marching framework to render these
scene volumes under arbitrary viewpoint and lighting. This allows us to
optimize the scene volumes to minimize the error between their rendered images
and the captured images. Our method is able to reconstruct real scenes with
challenging non-Lambertian reflectance and complex geometry with occlusions and
shadowing. Moreover, it accurately generalizes to novel viewpoints and
lighting, including non-collocated lighting, rendering photorealistic images
that are significantly better than state-of-the-art mesh-based methods. We also
show that our learned reflectance volumes are editable, allowing for modifying
the materials of the captured scenes.

本文提出了一种基于深度学习的场景重建方法，应用了一种新颖的体积场景表示方法 —— 反射体积网格，通过物理可行的可微分体积光线行走框架，渲染出高质量的图像，实现了对具备挑战性的真实场景的重建，而重建得到的反射体积网格可进行编辑，可用于修改捕捉的场景材料。