  This paper studies how to flexibly integrate reconstructed 3D models into
practical 3D modeling pipelines such as 3D scene creation and rendering. Due to
the technical difficulty, one can only obtain rough 3D models (R3DMs) for most
real objects using existing 3D reconstruction techniques. As a result,
physically-based rendering (PBR) would render low-quality images or videos for
scenes that are constructed by R3DMs. One promising solution would be
representing real-world objects as Neural Fields such as NeRFs, which are able
to generate photo-realistic renderings of an object under desired viewpoints.
However, a drawback is that the synthesized views through Neural Fields
Rendering (NFR) cannot reflect the simulated lighting details on R3DMs in PBR
pipelines, especially when object interactions in the 3D scene creation cause
local shadows. To solve this dilemma, we propose a lighting transfer network
(LighTNet) to bridge NFR and PBR, such that they can benefit from each other.
LighTNet reasons about a simplified image composition model, remedies the
uneven surface issue caused by R3DMs, and is empowered by several
perceptual-motivated constraints and a new Lab angle loss which enhances the
contrast between lighting strength and colors. Comparisons demonstrate that
LighTNet is superior in synthesizing impressive lighting, and is promising in
pushing NFR further in practical 3D modeling workflows.
