Quality of NeRF Changes with the Viewing Path an Observer Takes: A Subjective Quality Assessment of Real-time NeRF Model

Despite Neural Radiance Fields (NeRF) revolutionizing the 3D world with its free viewpoint synthesis, its quality evaluation still relies on static image datasets. While a limited number of studies have challenged this conventional evaluation process with video-based experiments, our approach introduces a well-structured subjective assessment emphasizing the importance of assessing NeRF scenes through multiple dynamic video sequences. A dataset "NeRF-4Scenes" is prepared that includes four scenes covering large areas from indoor and outdoor environments. The method generates three different camera trajectories within each scene and evaluates three NeRF models through pairwise comparison. Our comprehensive analysis has demonstrated the significance of evaluating NeRF scenes from dynamic viewpoints. The changing preferences among observers across varied paths highlight the impacts of evaluating NeRF scenes from multiple viewpoints. The dataset is available for download at https://www.ntnu.edu/colourlab/software.