# Perceptual Quality Assessment of NeRF and Neural View Synthesis Methods for Front-Facing Views

arXiv: https://arxiv.org/abs/2303.15206 · HTML: https://mlsanity.com/paper/2303.15206/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 75 references that are in the map.
- First posted on arXiv 24 Mar 2023.

## Builds on (most cited first, 23 of 23)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fields](https://mlsanity.com/paper/2111.12077/) (2021)
- [Plenoxels: Radiance Fields without Neural Networks](https://mlsanity.com/paper/2112.05131/) (2021)
- [Direct Voxel Grid Optimization: Super-fast Convergence for Radiance Fields Reconstruction](https://mlsanity.com/paper/2111.11215/) (2021)
- [Neural Sparse Voxel Fields](https://mlsanity.com/paper/2007.11571/) (2020)
- [Image Quality Assessment: Unifying Structure and Texture Similarity](https://mlsanity.com/paper/2004.07728/) (2020)
- [IBRNet: Learning Multi-View Image-Based Rendering](https://mlsanity.com/paper/2102.13090/) (2021)
- [Local Light Field Fusion: Practical View Synthesis with Prescriptive Sampling Guidelines](https://mlsanity.com/paper/1905.00889/) (2019)
- [BlendedMVS: A Large-scale Dataset for Generalized Multi-view Stereo Networks](https://mlsanity.com/paper/1911.10127/) (2019)
- [DeepView: View Synthesis with Learned Gradient Descent](https://mlsanity.com/paper/1906.07316/) (2019)
- [NeX: Real-time View Synthesis with Neural Basis Expansion](https://mlsanity.com/paper/2103.05606/) (2021)
- [Stereo Magnification: Learning View Synthesis using Multiplane Images](https://mlsanity.com/paper/1805.09817/) (2018)
- [Is Attention All That NeRF Needs?](https://mlsanity.com/paper/2207.13298/) (2022)
- [Light Field Neural Rendering](https://mlsanity.com/paper/2112.09687/) (2021)
- [Neural Point Catacaustics for Novel-View Synthesis of Reflections](https://mlsanity.com/paper/2301.01087/) (2023)
- [A practical guide and software for analysing pairwise comparison experiments](https://mlsanity.com/paper/1712.03686/) (2017)
- [Active Sampling for Pairwise Comparisons via Approximate Message Passing and Information Gain Maximization](https://mlsanity.com/paper/2004.05691/) (2020)
- [ScanNeRF: a Scalable Benchmark for Neural Radiance Fields](https://mlsanity.com/paper/2211.13762/) (2022)
- [Benchmarking Robustness in Neural Radiance Fields](https://mlsanity.com/paper/2301.04075/) (2023)
- [Noise-Aware Merging of High Dynamic Range Image Stacks without Camera Calibration](https://mlsanity.com/paper/2009.07975/) (2020)
- [Virtual Rephotography: Novel View Prediction Error for 3D Reconstruction](https://mlsanity.com/paper/1601.06950/) (2016)

## Built on by (most cited first, 17 of 17)

- [NerfBaselines: Consistent and Reproducible Evaluation of Novel View Synthesis Methods](https://mlsanity.com/paper/2406.17345/) (2024)
- [RayGauss: Volumetric Gaussian-Based Ray Casting for Photorealistic Novel View Synthesis](https://mlsanity.com/paper/2408.03356/) (2024)
- [NeRF-NQA: No-Reference Quality Assessment for Scenes Generated by NeRF and Neural View Synthesis Methods](https://mlsanity.com/paper/2412.08029/) (2024)
- [Semantically-aware Neural Radiance Fields for Visual Scene Understanding: A Comprehensive Review](https://mlsanity.com/paper/2402.11141/) (2024)
- [GS-QA: Comprehensive Quality Assessment Benchmark for Gaussian Splatting View Synthesis](https://mlsanity.com/paper/2502.13196/) (2025)
- [Explicit-NeRF-QA: A Quality Assessment Database for Explicit NeRF Model Compression](https://mlsanity.com/paper/2407.08165/) (2024)
- [3DGS-IEval-15K: A Large-scale Image Quality Evaluation Database for 3D Gaussian-Splatting](https://mlsanity.com/paper/2506.14642/) (2025)
- [NVS-SQA: Exploring Self-Supervised Quality Representation Learning for Neurally Synthesized Scenes without References](https://mlsanity.com/paper/2501.06488/) (2025)
- [Benchmarking Image Similarity Metrics for Novel View Synthesis Applications](https://mlsanity.com/paper/2506.12563/) (2025)
- [3DGS-VBench: A Comprehensive Video Quality Evaluation Benchmark for 3DGS Compression](https://mlsanity.com/paper/2508.07038/) (2025)
- [Quality Prediction of AI Generated Images and Videos: Emerging Trends and Opportunities](https://mlsanity.com/paper/2410.08534/) (2024)
- [In Depth We Trust: Reliable Monocular Depth Supervision for Gaussian Splatting](https://mlsanity.com/paper/2604.05715/) (2026)
- [Search-to-World: Evaluation of 3D World Delivery from User Request through Web Search](https://mlsanity.com/paper/2609.07605/) (2026)
- [SCORP: Scene-Consistent Object Refinement via Proxy Generation and Tuning](https://mlsanity.com/paper/2506.23835/) (2025)
- [Exploring Metric Fusion for Evaluation of NeRFs](https://mlsanity.com/paper/2509.12836/) (2025)
- [Aes3D: Aesthetic Assessment in 3D Gaussian Splatting](https://mlsanity.com/paper/2605.05155/) (2026)
- [Learning View-Dependent Splatting Kernels](https://mlsanity.com/paper/2605.25426/) (2026)

## Abstract (arXiv)

Neural view synthesis (NVS) is one of the most successful techniques for synthesizing free viewpoint videos, capable of achieving high fidelity from only a sparse set of captured images. This success has led to many variants of the techniques, each evaluated on a set of test views typically using image quality metrics such as PSNR, SSIM, or LPIPS. There has been a lack of research on how NVS methods perform with respect to perceived video quality. We present the first study on perceptual evaluation of NVS and NeRF variants. For this study, we collected two datasets of scenes captured in a controlled lab environment as well as in-the-wild. In contrast to existing datasets, these scenes come with reference video sequences, allowing us to test for temporal artifacts and subtle distortions that are easily overlooked when viewing only static images. We measured the quality of videos synthesized by several NVS methods in a well-controlled perceptual quality assessment experiment as well as with many existing state-of-the-art image/video quality metrics. We present a detailed analysis of the results and recommendations for dataset and metric selection for NVS evaluation.