ImmersiveNeRF: Hybrid Radiance Fields for Unbounded Immersive Light
  Field Reconstruction
Xiaohang Yu Haoxiang Wang Yuqi Han Lei Yang Tao Yu Qionghai Dai
This paper proposes a hybrid radiance field representation for unbounded
immersive light field reconstruction which supports high-quality rendering and
aggressive view extrapolation. The key idea is to first formally separate the
foreground and the background and then adaptively balance learning of them
during the training process. To fulfill this goal, we represent the foreground
and background as two separate radiance fields with two different spatial
mapping strategies. We further propose an adaptive sampling strategy and a
segmentation regularizer for more clear segmentation and robust convergence.
Finally, we contribute a novel immersive light field dataset, named
THUImmersive, with the potential to achieve much larger space 6DoF immersive
rendering effects compared with existing datasets, by capturing multiple
neighboring viewpoints for the same scene, to stimulate the research and AR/VR
applications in the immersive light field domain. Extensive experiments
demonstrate the strong performance of our method for unbounded immersive light
field reconstruction.