  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.
