We present EgoNeRF, a practical solution to reconstruct large-scale real-world environments for VR assets. Given a few seconds of casually captured 360 video, EgoNeRF can efficiently build neural radiance fields which enable high-quality rendering from novel viewpoints. Motivated by the recent acceleration of NeRF using feature grids, we adopt spherical coordinate instead of conventional Cartesian coordinate. Cartesian feature grid is inefficient to represent large-scale unbounded scenes because it has a spatially uniform resolution, regardless of distance from viewers. The spherical parameterization better aligns with the rays of egocentric images, and yet enables factorization for performance enhancement. However, the na\"ive spherical grid suffers from irregularities at two poles, and also cannot represent unbounded scenes. To avoid singularities near poles, we combine two balanced grids, which results in a quasi-uniform angular grid. We also partition the radial grid exponentially and place an environment map at infinity to represent unbounded scenes. Furthermore, with our resampling technique for grid-based methods, we can increase the number of valid samples to train NeRF volume. We extensively evaluate our method in our newly introduced synthetic and real-world egocentric 360 video datasets, and it consistently achieves state-of-the-art performance.

翻译：我们提出EgoNeRF，一种用于重建大规模真实世界VR环境资产的实用解决方案。给定数秒随意拍摄的360度视频，EgoNeRF能够高效构建神经辐射场，实现从新视角的高质量渲染。受近期利用特征网格加速NeRF技术的启发，我们采用球面坐标替代传统笛卡尔坐标。笛卡尔特征网格在表示大规模无界场景时效率低下，因其空间分辨率均匀，与观察者距离无关。球面参数化能更好地对齐自我中心图像的射线方向，同时支持因子分解以提升性能。然而，朴素球面网格在两极存在不规则性，且无法表示无界场景。为避免极点附近的奇异性，我们结合两个平衡网格，形成准均匀角网格。同时，采用指数方式划分径向网格，并在无穷远处放置环境贴图以表示无界场景。此外，通过针对网格方法的重采样技术，我们可增加有效样本数以训练NeRF体。我们在新引入的合成和真实世界自我中心360度视频数据集上进行了广泛评估，该方法持续取得最先进性能。