Mip-NeRF 360

Official Mip-NeRF 360 implementation addapted to handle different camera distortion/intrinsic parameters. It was designed for unbounded object-centric 360-degree capture and handles anti-aliasing well. It is, however slower to train and render compared to other approaches.

Web: https://jonbarron.info/mipnerf360/

Paper: Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fields

Authors: Jonathan T. Barron, Ben Mildenhall, Dor Verbin, Pratul P. Srinivasan, Peter Hedman

Mip-NeRF 360

Mip-NeRF 360 is a collection of four indoor and five outdoor object-centric scenes. The camera trajectory is an orbit around the object with fixed elevation and radius. The test set takes each n-th frame of the trajectory as test views.

	Scene	PSNR	SSIM	LPIPS	Time	GPU Mem.
	average				7h 29m 42s	126.99 GB

Blender

Blender (nerf-synthetic) is a synthetic dataset used to benchmark NeRF methods. It consists of 8 scenes of an object placed on a white background. Cameras are placed on a semi-sphere around the object.

	Scene	PSNR	SSIM	LPIPS	Time	GPU Mem.
	average				3h 29m 39s	114.80 GB