# Explicit-NeRF-QA: A Quality Assessment Database for Explicit NeRF Model Compression

arXiv: https://arxiv.org/abs/2407.08165 · HTML: https://mlsanity.com/paper/2407.08165/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 41 references that are in the map.
- First posted on arXiv 11 Jul 2024.

## Builds on (most cited first, 19 of 19)

- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Instant Neural Graphics Primitives with a Multiresolution Hash Encoding](https://mlsanity.com/paper/2201.05989/) (2022)
- [Plenoxels: Radiance Fields without Neural Networks](https://mlsanity.com/paper/2112.05131/) (2021)
- [TensoRF: Tensorial Radiance Fields](https://mlsanity.com/paper/2203.09517/) (2022)
- [Exploring CLIP for Assessing the Look and Feel of Images](https://mlsanity.com/paper/2207.12396/) (2022)
- [Direct Voxel Grid Optimization: Super-fast Convergence for Radiance Fields Reconstruction](https://mlsanity.com/paper/2111.11215/) (2021)
- [Image Quality Assessment: Unifying Structure and Texture Similarity](https://mlsanity.com/paper/2004.07728/) (2020)
- [Local Light Field Fusion: Practical View Synthesis with Prescriptive Sampling Guidelines](https://mlsanity.com/paper/1905.00889/) (2019)
- [Variable Bitrate Neural Fields](https://mlsanity.com/paper/2206.07707/) (2022)
- [Compressible-composable NeRF via Rank-residual Decomposition](https://mlsanity.com/paper/2205.14870/) (2022)
- [Compressing Volumetric Radiance Fields to 1 MB](https://mlsanity.com/paper/2211.16386/) (2022)
- [Masked Wavelet Representation for Compact Neural Radiance Fields](https://mlsanity.com/paper/2212.09069/) (2022)
- [Compressing Explicit Voxel Grid Representations: fast NeRFs become also small](https://mlsanity.com/paper/2210.12782/) (2022)
- [Perceptual Quality Assessment of NeRF and Neural View Synthesis Methods for Front-Facing Views](https://mlsanity.com/paper/2303.15206/) (2023)
- [TSMD: A Database for Static Color Mesh Quality Assessment Study](https://mlsanity.com/paper/2308.01940/) (2023)
- [SJTU-TMQA: A quality assessment database for static mesh with texture map](https://mlsanity.com/paper/2309.15675/) (2023)
- [NeRF View Synthesis: Subjective Quality Assessment and Objective Metrics Evaluation](https://mlsanity.com/paper/2405.20078/) (2024)
- [NeRF-QA: Neural Radiance Fields Quality Assessment Database](https://mlsanity.com/paper/2305.03176/) (2023)
- [TDMD: A Database for Dynamic Color Mesh Subjective and Objective Quality Explorations](https://mlsanity.com/paper/2308.01499/) (2023)

## Built on by (most cited first, 8 of 8)

- [A Hierarchical Compression Technique for 3D Gaussian Splatting Compression](https://mlsanity.com/paper/2411.06976/) (2024)
- [GS-QA: Comprehensive Quality Assessment Benchmark for Gaussian Splatting View Synthesis](https://mlsanity.com/paper/2502.13196/) (2025)
- [3DGS-IEval-15K: A Large-scale Image Quality Evaluation Database for 3D Gaussian-Splatting](https://mlsanity.com/paper/2506.14642/) (2025)
- [3DGS-VBench: A Comprehensive Video Quality Evaluation Benchmark for 3DGS Compression](https://mlsanity.com/paper/2508.07038/) (2025)
- [Non-Aligned Reference Image Quality Assessment for Novel View Synthesis](https://mlsanity.com/paper/2511.08155/) (2025)
- [Exploring Metric Fusion for Evaluation of NeRFs](https://mlsanity.com/paper/2509.12836/) (2025)
- [SpatialQ: Understanding 3D Gaussian Splatting Scene Quality via Visual-based MLLM](https://mlsanity.com/paper/2607.26595/) (2026)
- [From Explicit References to Scene Manifolds: Distributional Fidelity and Realism for Radiance Field Quality Assessment](https://mlsanity.com/paper/2609.07346/) (2026)

## Abstract (arXiv)

In recent years, Neural Radiance Fields (NeRF) have demonstrated significant advantages in representing and synthesizing 3D scenes. Explicit NeRF models facilitate the practical NeRF applications with faster rendering speed, and also attract considerable attention in NeRF compression due to its huge storage cost. To address the challenge of the NeRF compression study, in this paper, we construct a new dataset, called Explicit-NeRF-QA. We use 22 3D objects with diverse geometries, textures, and material complexities to train four typical explicit NeRF models across five parameter levels. Lossy compression is introduced during the model generation, pivoting the selection of key parameters such as hash table size for InstantNGP and voxel grid resolution for Plenoxels. By rendering NeRF samples to processed video sequences (PVS), a large scale subjective experiment with lab environment is conducted to collect subjective scores from 21 viewers. The diversity of content, accuracy of mean opinion scores (MOS), and characteristics of NeRF distortion are comprehensively presented, establishing the heterogeneity of the proposed dataset. The state-of-the-art objective metrics are tested in the new dataset. Best Person correlation, which is around 0.85, is collected from the full-reference objective metric. All tested no-reference metrics report very poor results with 0.4 to 0.6 correlations, demonstrating the need for further development of more robust no-reference metrics. The dataset, including NeRF samples, source 3D objects, multiview images for NeRF generation, PVSs, MOS, is made publicly available at the following location: https://github.com/YukeXing/Explicit-NeRF-QA.