NeRF synthesis with shading guidance
Chenbin Li Yu Xin Gaoyi Liu Xiang Zeng Ligang Liu
The emerging Neural Radiance Field (NeRF) shows great potential in
representing 3D scenes, which can render photo-realistic images from novel view
with only sparse views given. However, utilizing NeRF to reconstruct real-world
scenes requires images from different viewpoints, which limits its practical
application. This problem can be even more pronounced for large scenes. In this
paper, we introduce a new task called NeRF synthesis that utilizes the
structural content of a NeRF patch exemplar to construct a new radiance field
of large size. We propose a two-phase method for synthesizing new scenes that
are continuous in geometry and appearance. We also propose a boundary
constraint method to synthesize scenes of arbitrary size without artifacts.
Specifically, we control the lighting effects of synthesized scenes using
shading guidance instead of decoupling the scene. We have demonstrated that our
method can generate high-quality results with consistent geometry and
appearance, even for scenes with complex lighting. We can also synthesize new
scenes on curved surface with arbitrary lighting effects, which enhances the
practicality of our proposed NeRF synthesis approach.