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Recent advances in Neural Radiance Fields (NeRF) have demonstratedsignificant potential for representing 3D scene appearances as implicit neuralnetworks, enabling the synthesis of high-fidelity novel views. However, thelengthy training and rendering process hinders the widespread adoption of thispromising technique for real-time rendering applications. To address thisissue, we present an effective adaptive multi-NeRF method designed toaccelerate the neural rendering process for large scenes with unbalancedworkloads due to varying scene complexities.  Our method adaptively subdivides scenes into axis-aligned bounding boxesusing a tree hierarchy approach, assigning smaller NeRFs to different-sizedsubspaces based on the complexity of each scene portion. This ensures theunderlying neural representation is specific to a particular part of the scene.We optimize scene subdivision by employing a guidance density grid, whichbalances representation capability for each Multilayer Perceptron (MLP).Consequently, samples generated by each ray can be sorted and collected forparallel inference, achieving a balanced workload suitable for small MLPs withconsistent dimensions for regular and GPU-friendly computations. We aosldemonstrated an efficient NeRF sampling strategy that intrinsically adapts toincrease parallelism, utilization, and reduce kernel calls, thereby achievingmuch higher GPU utilization and accelerating the rendering process.

Adaptive Multi-NeRF: Exploit Efficient Parallelism in Adaptive Multiple Scale Neural Radiance Field Rendering