Datasets:

nihermann
/

annotated-3DGS-artifacts

Tasks:

Image Segmentation

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Tags:

3d-reconstruction

artifact-detection

image-quality-assessment

human-annotation

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Dataset card Data Studio Files Files and versions

nihermann commited on Oct 2

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 > Authors: Nicolai Hermann, Jorge Condor, and Piotr Didyk
 ### Dataset Description
-The Dataset consists of 36 hand-selected 3D Gaussian Splatting renderings containing common reconstruction artefacts, ground truths, human-annotated masks, and a set of reference views of the same scene.
 Each mask is an average of 22 binary masks, each created by a different human participant who was asked to annotate areas in the reconstructed images that they perceived as visually degraded, unnatural, or incongruent. The dataset can be used to benchmark No-Reference, Cross-Reference, and Full-Reference image quality metrics for their correlation with human judgment. The naming convention of the data is as follows:

 > Authors: Nicolai Hermann, Jorge Condor, and Piotr Didyk
 ### Dataset Description
+The Dataset consists of 36 hand-selected 3D Gaussian Splatting renderings containing common reconstruction artefacts, (aligned) ground truths, human-annotated masks, and a set of unaligned reference views of the same scene.
 Each mask is an average of 22 binary masks, each created by a different human participant who was asked to annotate areas in the reconstructed images that they perceived as visually degraded, unnatural, or incongruent. The dataset can be used to benchmark No-Reference, Cross-Reference, and Full-Reference image quality metrics for their correlation with human judgment. The naming convention of the data is as follows: