NerfStudio

NerfStudio (Nerfacto) is a method based on Instant-NGP which combines several improvements from different papers to achieve good quality on real-world scenes captured under normal conditions. It is fast to train (12 min) and render speed is ~1 FPS.

Web: https://docs.nerf.studio/
Paper: Nerfstudio: A Modular Framework for Neural Radiance Field Development
Authors: Matthew Tancik, Ethan Weber, Evonne Ng, Ruilong Li, Brent Yi, Justin Kerr, Terrance Wang, Alexander Kristoffersen, Jake Austin, Kamyar Salahi, Abhik Ahuja, David McAllister, Angjoo Kanazawa

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.
average19m 50s3.80 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.
average9m 38s3.65 GB

Nerfstudio

Nerfstudio Dataset includes 10 in-the-wild captures obtained using either a mobile phone or a mirror-less camera with a fisheye lens. We processed the data using either COLMAP or the Polycam app to obtain camera poses and intrinsic parameters.

Scene
PSNR
SSIM
LPIPS
Time
GPU Mem.
average13m 30s4.77 GB