Accurate, Dense, and Robust Multiview Stereopsis - Department of ...
Accurate, Dense, and Robust Multiview Stereopsis - Department of ...
Accurate, Dense, and Robust Multiview Stereopsis - Department of ...
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FURUKAWA AND PONCE: ACCURATE, DENSE, AND ROBUST MULTIVIEW STEREOPSIS 1373<br />
Fig. 16. Final mesh models: From left to right <strong>and</strong> top to bottom: roman, temple, dino, skull, face-1, face-2, body, city-hall, wall, fountain, brussels,<br />
steps-1, steps-2, steps-3, <strong>and</strong> castle data sets. Note that the mesh models are rendered from multiple view points for fountain <strong>and</strong> castle data sets to<br />
show their overall structure.<br />
high <strong>and</strong> disparities instead <strong>of</strong> depth values are typically<br />
estimated per image [38].<br />
6 CONCLUSION AND FUTURE WORK<br />
We have proposed a novel algorithm for calibrated multiview<br />
stereo that outputs a dense set <strong>of</strong> patches covering the surface<br />
<strong>of</strong> an object or a scene observed by multiple calibrated<br />
photographs. The algorithm starts by detecting features in<br />
each image, matches them across multiple images to form an<br />
initial set <strong>of</strong> patches, <strong>and</strong> uses an expansion procedure to<br />
obtain a denser set <strong>of</strong> patches before using visibility<br />
constraints to filter away false matches. After converting the<br />
resulting patch model into a mesh appropriate for imagebased<br />
modeling, an optional refinement algorithm can be<br />
used to refine the mesh, <strong>and</strong> achieve even higher accuracy.<br />
Our approach can h<strong>and</strong>le a variety <strong>of</strong> data sets <strong>and</strong> allows<br />
outliers or obstacles in the images. Furthermore, it does not