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.

翻译：摘要：神经辐射场（NeRF）的最新进展展示了其作为隐式神经网络表示三维场景外观的巨大潜力，能够合成高保真新视角图像。然而，漫长的训练和渲染过程阻碍了该技术在实时渲染应用中的广泛采用。为解决此问题，我们提出一种高效的自适应多NeRF方法，旨在加速因场景复杂度变化导致工作负载不均的大型场景的神经渲染过程。我们的方法采用树层次结构将场景自适应细分为轴对齐包围盒，并根据每个场景部分的复杂度为不同大小的子空间分配较小的NeRF，确保底层神经表示专用于场景特定部分。通过使用指导密度网格优化场景细分，平衡每个多层感知器（MLP）的表示能力。由此，每条光线生成的样本可被排序收集用于并行推理，实现适用于维度一致、支持规则化GPU友好计算的小型MLP的均衡工作负载。我们还展示了高效的NeRF采样策略，该策略通过内在适应性提升并行性、利用率并减少核函数调用，从而显著提高GPU利用率并加速渲染过程。