Neural Radiance Fields
Neural Radiance Fields (NeRF) is a method for synthesizing novel views of complex scenes by optimizing an underlying continuous volumetric scene function using a sparse set of input views. The dataset contains three parts with the first 2 being synthetic renderings of objects called Diffuse Synthetic 360◦ and Realistic Synthetic 360◦ while the third is real images of complex scenes. Diffuse Synthetic 360◦ consists of four Lambertian objects with simple geometry. Each object is rendered at 512x512 pixels from viewpoints sampled on the upper hemisphere. Realistic Synthetic 360◦ consists of eight objects of complicated geometry and realistic non-Lambertian materials. Six of them are rendered from viewpoints sampled on the upper hemisphere and the two left are from viewpoints sampled on a full sphere with all of them at 800x800 pixels. The real images of complex scenes consist of 8 forward-facing scenes captured with a cellphone at a size of 1008x756 pixels.
Variants: NeRF
This dataset is used in 1 benchmark:
Recent papers with results on this dataset: