2 research outputs found
Towards a Robust Framework for NeRF Evaluation
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.Comment: 9 pages, 4 experiment