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。

自适应多 NeRF：利用自适应多尺度神经辐射场渲染中的高效并行性

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

This paper proposes a novel graph-constrained generative adversarial network,
whose generator and discriminator are built upon relational architecture. The
main idea is to encode the constraint into the graph structure of its
relational networks. We have demonstrated the proposed architecture for a new
house layout generation problem, whose task is to take an architectural
constraint as a graph (i.e., the number and types of rooms with their spatial
adjacency) and produce a set of axis-aligned bounding boxes of rooms. We
measure the quality of generated house layouts with the three metrics: the
realism, the diversity, and the compatibility with the input graph constraint.
Our qualitative and quantitative evaluations over 117,000 real floorplan images
demonstrate that the proposed approach outperforms existing methods and
baselines. We will publicly share all our code and data.

本文提出了一种基于关系架构的新型图约束生成对抗网络，其生成器和鉴别器利用关系结构进行建模，旨在将约束编码到其关系网络的图结构中，将建筑约束作为图形输入，并生成一套轴对齐的房间边框，通过 117000 张实际的平面图像的定量和定性评估表明，该方法优于现有的方法和基线。