3D Reconstruction and New View Synthesis of Indoor Environments based on
  a Dual Neural Radiance Field
Zhenyu Bao Guibiao Liao Zhongyuan Zhao Kanglin Liu Qing Li Guoping Qiu
Simultaneously achieving 3D reconstruction and new view synthesis for indoor
environments has widespread applications but is technically very challenging.
State-of-the-art methods based on implicit neural functions can achieve
excellent 3D reconstruction results, but their performances on new view
synthesis can be unsatisfactory. The exciting development of neural radiance
field (NeRF) has revolutionized new view synthesis, however, NeRF-based models
can fail to reconstruct clean geometric surfaces. We have developed a dual
neural radiance field (Du-NeRF) to simultaneously achieve high-quality geometry
reconstruction and view rendering. Du-NeRF contains two geometric fields, one
derived from the SDF field to facilitate geometric reconstruction and the other
derived from the density field to boost new view synthesis. One of the
innovative features of Du-NeRF is that it decouples a view-independent
component from the density field and uses it as a label to supervise the
learning process of the SDF field. This reduces shape-radiance ambiguity and
enables geometry and color to benefit from each other during the learning
process. Extensive experiments demonstrate that Du-NeRF can significantly
improve the performance of novel view synthesis and 3D reconstruction for
indoor environments and it is particularly effective in constructing areas
containing fine geometries that do not obey multi-view color consistency.