KITTI-360

Method

Depth Guided Generalizable NeRF for Sparse-View Street Scenes [DGNerf]
[Anonymous Submission]

Submitted on 3 Mar. 2024 10:11 by
[Anonymous Submission]

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

Method Description:
Our method utilizes predicted noisy but easily accessible depth maps to
form a generalizable NeRF, enabling strong generalization even with highly
sparse views.
Parameters:
alpha=0.2
Latex Bibtex:
NA

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
17.33 0.714 0.397
This table as LaTeX

Test Image 0

Prediction

Error


Test Image 1

Prediction

Error


Test Image 2

Prediction

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Test Image 3

Prediction

Error


Test Image 4

Prediction

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Test Image 5

Prediction

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Test Image 6

Prediction

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Test Image 7

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Test Image 8

Prediction

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Test Image 9

Prediction

Error





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