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.

本文提出了一种混合辐射场表示方法，用于无界沉浸式光场重建，支持高质量渲染和激进的视点外推。主要思想是首先形式上将前景和背景分开，然后在训练过程中自适应地平衡对它们的学习。为了实现这一目标，我们将前景和背景表示为两个具有不同空间映射策略的辐射场。我们进一步提出了一种自适应采样策略和分割正则化器，以实现更清晰的分割和稳健的收敛。最后，我们贡献了一种新颖的沉浸式光场数据集，名为 THUImmersive，与现有数据集相比，具有实现更大空间 6DoF 沉浸式渲染效果的潜力，通过捕获同一场景的多个相邻视点，以刺激沉浸式光场领域的研究和增强现实 / 虚拟现实应用。广泛的实验证明了我们的方法在无界沉浸式光场重建方面的强大性能。