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Method

NeRF: Neural Radiance Fields [st] [NeRF]

Submitted on 11 Apr. 2023 15:32 by
Yiyi Liao (MPI)

Running time:		10 s
Environment:		1 core @ 2.5 Ghz (C/C++)

Method Description:

We evaluate NeRF using the pytorch implementation.

Parameters:

Default parameters are used.

Latex Bibtex:

@inproceedings{mildenhall2020nerf,
title={NeRF: Representing Scenes as Neural Radiance
Fields for View
Synthesis},
author={Ben Mildenhall and Pratul P. Srinivasan and
Matthew Tancik
and Jonathan T. Barron and Ravi Ramamoorthi and Ren
Ng},
year={2020},
booktitle={ECCV},
}

Detailed Results

This page provides detailed results for the method(s) selected. For the first 10 test images, we display the synthesized image and an error image. The error image visualizes the SSIM at every pixel. As the range of SSIM is within [-1,1] with 1 indicating the best performance and -1 indicating the worst, we visualize 1 - (1 + SSIM) / 2 such that bright region means large error and dark means low error.

Test Set Average

PSNR	SSIM	LPIPS
15.74	0.648	0.590

Error