[Paper Review] Towards a Robust Framework for NeRF Evaluation

Adrian Azzarelli, Nantheera Anantrasirichai+1 more|arXiv (Cornell University)|May 29, 2023

Visual perception and processing mechanisms4 citations

TL;DR

This paper proposes a robust, parametric evaluation framework for Neural Radiance Fields (NeRFs) by isolating the neural rendering network from the full pipeline and using ray-casting on mesh-based representations to generate accurate ground truth. The framework introduces the Whole-scene Average Prediction Error (WAPE) metric and a task complexity metric, demonstrating superior objective evaluation over conventional metrics like PSNR, with SIREN showing the best performance in both WAPE and visual quality.

ABSTRACT

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.

Motivation & Objective

• To address the lack of reliable, objective evaluation methods for NeRFs, which currently rely on subjective assessments and conventional image quality metrics.
• To isolate the neural rendering network from the full NeRF pipeline to enable precise, parametric evaluation of rendering performance.
• To develop a configurable ground truth synthesis method using ray-casting on mesh models to generate high-fidelity radiance field samples.
• To introduce a novel Whole-scene Average Prediction Error (WAPE) metric that quantifies prediction accuracy across colour and density fields.
• To define a task complexity metric accounting for view distribution, input sample count, and algorithmic complexity to better reflect learning difficulty.

Proposed method

• The framework decouples the neural rendering network from the full NeRF pipeline, enabling independent evaluation of the rendering component.
• Ground truth is synthesized by transforming triangular or rectangular mesh models into explicit NeRF samples via ray-casting and ray shading.
• The Whole-scene Average Prediction Error (WAPE) is computed as the mean absolute error between predicted and ground-truth colour and density values across all rays.
• A task complexity metric is proposed, incorporating the number of input samples, spatial distribution of training and novel views, and functional complexity of the ray-tracing algorithm.
• The method supports evaluation of both NeRFs and depth-wise implicit neural representations (INRs) by adapting input and output definitions accordingly.
• The framework is validated using NeRFStudio-compatible code and tested across multiple activation functions (ReLU, SIREN, WIRE, GARF) on a synthetic scene with intersecting meshes.

Figure 1 : Our proposed framework for evaluation. The Target Network is the rendering network generating NeRF colour and density fields. The data synthesis employs the Mesh to NeRF and Ray Shading modules to input the target network and to evaluate the colour and density predictions, respectively. F

Experimental results

Research questions

• RQ1 How can we objectively evaluate NeRF rendering performance independent of the full pipeline's error accumulation?
• RQ2 To what extent does the proposed WAPE metric better reflect rendering accuracy than conventional metrics like PSNR?
• RQ3 Can a configurable ground truth synthesis method based on mesh-to-NeRF ray-casting produce reliable and high-quality reference data for evaluation?
• RQ4 How does task complexity—defined by view distribution and input sample count—affect the learning difficulty of INRs and NeRFs?
• RQ5 Which activation function (e.g., SIREN, ReLU) yields the most accurate and stable predictions under the proposed evaluation framework?

Key findings

• SIREN achieved the lowest WAPE score of 0.092 ± 0.017, outperforming ReLU (0.101 ± 0.024), WIRE (0.109 ± 0.021), and GAUSS (0.185 ± 0.019), indicating superior prediction accuracy.
• Despite a higher PSNR of 44.0 ± 9.6, the ReLU+encoder method produced noisy predictions and struggled with precise geometric placement, highlighting PSNR's limitations in capturing spatial accuracy.
• The Gaussian method achieved a high PSNR (42.3 ± 6.0) but had a significantly higher WAPE (0.185 ± 0.019), indicating it underestimated large portions of the scene, which PSNR fails to penalize adequately.
• SIREN produced the smoothest and most structured visual estimates, especially in high-frequency regions, aligning with its superior WAPE performance.
• The proposed task complexity metric effectively captures differences in learning difficulty, with reduced complexity when training and novel views are spatially aligned.
• The framework demonstrates that conventional metrics like PSNR can be misleading, while WAPE provides a more reliable, objective measure of rendering quality.

Figure 2 : Example of a NeRF volumetric sample using Equation 1 to render volume samples falling along the same ray