IRIS: Inverse Rendering of Indoor Scenes from Low Dynamic Range Images
Zhi-Hao Lin Jia-Bin Huang Zhengqin Li Zhao Dong Christian Richardt Tuotuo Li Michael Zollhöfer Johannes Kopf Shenlong Wang Changil Kim
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.