Neural Radiance Field (NeRF) research has attracted significant attention recently, with 3D modelling, virtual/augmented reality, and visual effects driving its application. While current NeRF implementations can produce high quality visual results, there is a conspicuous lack of reliable methods for evaluating them. Conventional image quality assessment methods and analytical metrics (e.g. PSNR, SSIM, LPIPS etc.) only provide approximate indicators of performance since they generalise the ability of the entire NeRF pipeline. Hence, in this paper, we propose a new test framework which isolates the neural rendering network from the NeRF pipeline and then performs a parametric evaluation by training and evaluating the NeRF on an explicit radiance field representation. We also introduce a configurable approach for generating representations specifically for evaluation purposes. This employs ray-casting to transform mesh models into explicit NeRF samples, as well as to "shade" these representations. Combining these two approaches, we demonstrate how different "tasks" (scenes with different visual effects or learning strategies) and types of networks (NeRFs and depth-wise implicit neural representations (INRs)) can be evaluated within this framework. Additionally, we propose a novel metric to measure task complexity of the framework which accounts for the visual parameters and the distribution of the spatial data. Our approach offers the potential to create a comparative objective evaluation framework for NeRF methods.

翻译：神经辐射场（NeRF）研究近期受到广泛关注，其应用涵盖三维建模、虚拟/增强现实及视觉特效等领域。尽管现有NeRF实现可生成高质量视觉结果，但明显缺乏可靠的评估方法。传统图像质量评估方法与分析指标（如PSNR、SSIM、LPIPS等）仅能提供近似性能指标，因其泛化了整个NeRF管线的能力。为此，本文提出一种新型测试框架：将神经渲染网络从NeRF管线中分离，通过显式辐射场表示进行参数化评估，即在该表示上训练和评估NeRF。我们同时引入可配置的表示生成方法，专门用于评估目的——采用光线投射技术将网格模型转换为显式NeRF样本，并对这些表示进行"着色"。结合这两种方法，我们展示了如何在该框架内评估不同"任务"（具有不同视觉效果或学习策略的场景）与网络类型（NeRF及深度隐式神经表示（INR））。此外，我们提出一种新的度量标准，通过考虑视觉参数与空间数据分布来测量框架的任务复杂度。该方法为NeRF方法建立比较性客观评估框架提供了可能性。