  In this paper, we propose the first-ever real benchmark thought for
evaluating Neural Radiance Fields (NeRFs) and, in general, Neural Rendering
(NR) frameworks. We design and implement an effective pipeline for scanning
real objects in quantity and effortlessly. Our scan station is built with less
than 500$ hardware budget and can collect roughly 4000 images of a scanned
object in just 5 minutes. Such a platform is used to build ScanNeRF, a dataset
characterized by several train/val/test splits aimed at benchmarking the
performance of modern NeRF methods under different conditions. Accordingly, we
evaluate three cutting-edge NeRF variants on it to highlight their strengths
and weaknesses. The dataset is available on our project page, together with an
online benchmark to foster the development of better and better NeRFs.
