Neural radiance fields (NeRFs) enable high-quality novel view synthesis, but their high computational complexity limits deployability. While existing neural-based solutions strive for efficiency, they use one-size-fits-all architectures regardless of scene complexity. The same architecture may be unnecessarily large for simple scenes but insufficient for complex ones. Thus, there is a need to dynamically optimize the neural network component of NeRFs to achieve a balance between computational complexity and specific targets for synthesis quality. We introduce NAS-NeRF, a generative neural architecture search strategy that generates compact, scene-specialized NeRF architectures by balancing architecture complexity and target synthesis quality metrics. Our method incorporates constraints on target metrics and budgets to guide the search towards architectures tailored for each scene. Experiments on the Blender synthetic dataset show the proposed NAS-NeRF can generate architectures up to 5.74$\times$ smaller, with 4.19$\times$ fewer FLOPs, and 1.93$\times$ faster on a GPU than baseline NeRFs, without suffering a drop in SSIM. Furthermore, we illustrate that NAS-NeRF can also achieve architectures up to 23$\times$ smaller, with 22$\times$ fewer FLOPs, and 4.7$\times$ faster than baseline NeRFs with only a 5.3% average SSIM drop. Our source code is also made publicly available at https://saeejithnair.github.io/NAS-NeRF.

翻译：神经辐射场（NeRFs）能够实现高质量的新视角合成，但其高计算复杂度限制了部署能力。现有基于神经网络的解决方案虽致力于提升效率，却采用“一刀切”的架构，未考虑场景复杂性差异。相同的架构对简单场景可能过于庞大，而对复杂场景则力有不逮。因此，亟需动态优化NeRF的神经网络组件，以在计算复杂度与合成质量特定目标之间取得平衡。我们提出NAS-NeRF，一种生成式神经架构搜索策略，通过权衡架构复杂度与目标合成质量指标，生成紧凑且场景专用的NeRF架构。该方法将目标指标与预算约束融入搜索过程，引导生成针对每个场景定制的架构。在Blender合成数据集上的实验表明，与基线NeRF相比，所提NAS-NeRF可生成体积缩小至5.74倍、FLOPs减少至4.19倍、GPU运行速度提升至1.93倍的架构，且SSIM指标未下降。此外，我们证明NAS-NeRF还能实现体积缩小至23倍、FLOPs减少至22倍、GPU运行速度提升至4.7倍的架构，同时平均SSIM仅下降5.3%。我们的源代码已在https://saeejithnair.github.io/NAS-NeRF 公开提供。