Measuring visual closeness of 3-D models is an important issue for different problems and there is still no standardized metric or algorithm to do it.
The normal of a surface plays a vital role in the shading of a 3-D object. Motivated by this, we developed two applications to measure visualcloseness, introducing normal difference as a parameter in a weighted metric in Metro’s sampling approach to obtain the maximum and mean distance between 3-D models using 3-D and 6-D correspondence search structures.
A visual closeness metric should provide accurate information on what the human observers would perceive as visually close objects. We performed
a validation study with a group of people to evaluate the correlation of our
metrics with subjective perception. The results were positive since the metrics
predicted the subjective rankings more accurately than the Hausdorff
distance.
| Date of Award | Sep 2012 |
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| Original language | English (US) |
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| Awarding Institution | - Computer, Electrical and Mathematical Sciences and Engineering
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| Supervisor | Antoine Vigneron (Supervisor) |
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- 3-D models
- Visual closeness
- meshes
- Hausdorff distance