Existing neural radiance fields (NeRF) methods for large-scale scene modeling require days of training using multiple GPUs, hindering their applications in scenarios with limited computing resources. Despite fast optimization NeRF variants have been proposed based on the explicit dense or hash grid features, their effectivenesses are mainly demonstrated in object-scale scene representation. In this paper, we point out that the low feature resolution in explicit representation is the bottleneck for large-scale unbounded scene representation. To address this problem, we introduce a new and efficient hybrid feature representation for NeRF that fuses the 3D hash-grids and high-resolution 2D dense plane features. Compared with the dense-grid representation, the resolution of a dense 2D plane can be scaled up more efficiently. Based on this hybrid representation, we propose a fast optimization NeRF variant, called GP-NeRF, that achieves better rendering results while maintaining a compact model size. Extensive experiments on multiple large-scale unbounded scene datasets show that our model can converge in 1.5 hours using a single GPU while achieving results comparable to or even better than the existing method that requires about one day's training with 8 GPUs.

翻译：现有的用于大规模场景建模的神经辐射场（NeRF）方法需要多块GPU进行数天训练，限制了其在计算资源受限场景中的应用。尽管基于显式密集网格或哈希网格特征的快速优化NeRF变体已被提出，但其有效性主要体现于物体级场景表示。本文指出显式表示中的低特征分辨率是大规模无界场景表示的瓶颈。为解决该问题，我们提出一种融合3D哈希网格与高分辨率2D密集平面特征的高效混合特征表示方法。与密集网格表示相比，密集2D平面的分辨率可更高效地扩展。基于该混合表示，我们提出快速优化NeRF变体GP-NeRF，在保持紧凑模型尺寸的同时实现更优渲染效果。在多个大规模无界场景数据集上的实验表明，我们的模型可在单块GPU上于1.5小时内收敛，且达到甚至超越现有需8块GPU训练约一天的方法的效果。