Computer Laboratory



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 wellcontrolled 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.


  • Paper: Coming soon
  • Supplementary: Coming soon
  • Code: Coming soon
  • Dataset: [link]


This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement N◦ 765911 (RealVision) and from the Royal Society, award IES\R2\202141. This project is also supported by a UKRI Future Leaders Fellowship [grant number G104084]. We greatly thank Prof. Damiano Marchi and Dr. Simone Farina for granting access to collect NeRFs inside the Natural History Museum of the University of Pisa.