While numerous 3D reconstruction and novel-view synthesis methods allow for
photorealistic rendering of a scene from multi-view images easily captured with
consumer cameras, they bake illumination in their representations and fall
short of supporting advanced applications like material editing, relighting,
and virtual object insertion. The reconstruction of physically based material
properties and lighting via inverse rendering promises to enable such
applications.
However, most inverse rendering techniques require high dynamic range (HDR)
images as input, a setting that is inaccessible to most users. We present a
method that recovers the physically based material properties and
spatially-varying HDR lighting of a scene from multi-view, low-dynamic-range
(LDR) images. We model the LDR image formation process in our inverse rendering
pipeline and propose a novel optimization strategy for material, lighting, and
a camera response model. We evaluate our approach with synthetic and real
scenes compared to the state-of-the-art inverse rendering methods that take
either LDR or HDR input. Our method outperforms existing methods taking LDR
images as input, and allows for highly realistic relighting and object
insertion.

从多视角、低动态范围（LDR）图像中恢复场景的基于物理的材质属性和空间变化的高动态范围（HDR）照明，实现了高度逼真的重照和物体插入。

IRIS：从低动态范围图像中反渲染室内场景

IRIS: Inverse Rendering of Indoor Scenes from Low Dynamic Range Images

Reconstruction and intrinsic decomposition of scenes from captured imagery
would enable many applications such as relighting and virtual object insertion.
Recent NeRF based methods achieve impressive fidelity of 3D reconstruction, but
bake the lighting and shadows into the radiance field, while mesh-based methods
that facilitate intrinsic decomposition through differentiable rendering have
not yet scaled to the complexity and scale of outdoor scenes. We present a
novel inverse rendering framework for large urban scenes capable of jointly
reconstructing the scene geometry, spatially-varying materials, and HDR
lighting from a set of posed RGB images with optional depth. Specifically, we
use a neural field to account for the primary rays, and use an explicit mesh
(reconstructed from the underlying neural field) for modeling secondary rays
that produce higher-order lighting effects such as cast shadows. By faithfully
disentangling complex geometry and materials from lighting effects, our method
enables photorealistic relighting with specular and shadow effects on several
outdoor datasets. Moreover, it supports physics-based scene manipulations such
as virtual object insertion with ray-traced shadow casting.

提出一种新的逆向渲染框架，能够从一组 RGB 图像中联合重建场景几何、空间变化的材料和 HDR 照明，并支持物理场景操作，例如射线跟踪阴影投射。