Efficient Large-scale Scene Representation with a Hybrid of High-resolution Grid and Plane Features
This work addresses the computational bottleneck for researchers and practitioners applying NeRF to large-scale, unbounded scenes, representing an incremental improvement over existing fast optimization variants.
The paper tackles the problem of slow training times for neural radiance fields (NeRF) in large-scale scenes by introducing a hybrid feature representation combining 3D hash-grids and high-resolution 2D dense plane features, resulting in convergence in 1.5 hours on a single GPU with comparable or better rendering quality than methods requiring days on multiple GPUs.
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.