PARF: Primitive-Aware Radiance Fusion for Indoor Scene Novel View
  Synthesis
Haiyang Ying Baowei Jiang Jinzhi Zhang Di Xu Tao Yu Qionghai Dai Lu Fang
This paper proposes a method for fast scene radiance field reconstruction
with strong novel view synthesis performance and convenient scene editing
functionality. The key idea is to fully utilize semantic parsing and primitive
extraction for constraining and accelerating the radiance field reconstruction
process. To fulfill this goal, a primitive-aware hybrid rendering strategy was
proposed to enjoy the best of both volumetric and primitive rendering. We
further contribute a reconstruction pipeline conducts primitive parsing and
radiance field learning iteratively for each input frame which successfully
fuses semantic, primitive, and radiance information into a single framework.
Extensive evaluations demonstrate the fast reconstruction ability, high
rendering quality, and convenient editing functionality of our method.