Accurate, Dense, and Robust Multiview Stereopsis - Department of ...

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