Recent advances in Neural Radiance Fields (NeRF) have demonstrated
significant potential for representing 3D scene appearances as implicit neural
networks, enabling the synthesis of high-fidelity novel views. However, the
lengthy training and rendering process hinders the widespread adoption of this
promising technique for real-time rendering applications. To address this
issue, we present an effective adaptive multi-NeRF method designed to
accelerate the neural rendering process for large scenes with unbalanced
workloads due to varying scene complexities.
Our method adaptively subdivides scenes into axis-aligned bounding boxes
using a tree hierarchy approach, assigning smaller NeRFs to different-sized
subspaces based on the complexity of each scene portion. This ensures the
underlying neural representation is specific to a particular part of the scene.
We optimize scene subdivision by employing a guidance density grid, which
balances representation capability for each Multilayer Perceptron (MLP).
Consequently, samples generated by each ray can be sorted and collected for
parallel inference, achieving a balanced workload suitable for small MLPs with
consistent dimensions for regular and GPU-friendly computations. We aosl
demonstrated an efficient NeRF sampling strategy that intrinsically adapts to
increase parallelism, utilization, and reduce kernel calls, thereby achieving
much higher GPU utilization and accelerating the rendering process.

通过将场景自适应地细分为轴对齐的包围盒，并使用树层次结构方法将较小的 NeRF 分配给不同大小的子空间，该论文提出了一种有效的适应性多 NeRF 方法，以加速针对复杂场景的神经渲染过程，以实现对特定场景部分的精细神经表示。