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）研究近期引起了广泛关注，三维建模、虚拟/增强现实和视觉特效推动了其应用。尽管当前NeRF实现能生成高质量视觉结果，却明显缺乏可靠的评估方法。传统图像质量评估方法和分析指标（如PSNR、SSIM、LPIPS等）仅能提供近似性能指标，因为它们概括了整个NeRF管线的能力。为此，本文提出一种新的测试框架，将神经渲染网络从NeRF管线中分离，通过显式辐射场表示训练和评估NeRF来执行参数化评估。我们还引入了一种可配置的方法，专门生成用于评估的表示。该方法利用光线投射将网格模型转换为显式NeRF样本，并"着色"这些表示。结合这两种方法，我们展示了不同"任务"（具有不同视觉效果或学习策略的场景）和网络类型（NeRF和深度隐式神经表示（INR））如何在此框架内进行评估。此外，我们提出了一种新指标来衡量框架的任务复杂度，该指标考虑了视觉参数和空间数据的分布。我们的方法为创建NeRF方法的比较性客观评估框架提供了可能性。