No-Reference Quality Assessment of 3D Models Represented in Neural Radiance Fields and 3D Gaussian Splattings

The continuous breakthroughs in 3D reconstruction and rendering technologies have enabled synthesized 3D models to achieve exceptional realism. In particular, Neural Radiance Fields (NeRF) and 3D Gaussian Splattings (3DGS) have gained significant attention due to their impressive ability to deliver high-quality 3D models. However, research on the quality assessment of NeRF/3DGS models remains an underexplored area, hindering the development of relevant generation, compression, and transmission algorithms. To fill this gap, in this paper, we propose the first no-reference quality assessment metric for NeRF/3DGS models rendered in Processed Video Sequences (PVS). Considering the uniqueness and diversity of distortions introduced in NeRF/3DGS models, the core idea behind the proposed metric is to extract universal quality-aware features that are generalizable across various distortions, rather than targeting a specific one. Specifically, inspired by the fact that spatial distortions typically alter the statistical distributions, we first measure the spatial fidelity of the rendered PVS by analyzing the spatial explicit statistics of textural variation and naturalness. Then, motivated by the ability of implicit energy composition changes to reflect temporal distortions, we propose to evaluate the temporal consistency of the rendered PVS through inter-frame discrepancy energy and multi-frame motion energy in the Singular Value Decomposition (SVD) domain. Finally, the spatial explicit statistics and temporal implicit energy are combined as perceptual features to evaluate the quality of NeRF/3DGS models via Support Vector Regression (SVR). Extensive experimental results on three representative databases demonstrate the superiority of the proposed metric in various aspects, such as predictive accuracy, performance stability, and cross-database generalizability. The source code will be publicly available at https://github.com/ZhengyuZhang96/PVS-3DMQA.