Tetra-NeRF

Tetra-NeRF is a method that represents the scene as tetrahedral mesh obtained using Delaunay tetrahedralization. The input point cloud has to be provided (for COLMAP datasets the point cloud is automatically extracted). This is the official implementation from the paper.

Web: https://jkulhanek.com/tetra-nerf

Paper: Tetra-NeRF: Representing Neural Radiance Fields Using Tetrahedra

Authors: Jonas Kulhanek, Torsten Sattler

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				17h 32m 35s	13.37 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				6h 53m 20s	29.57 GB