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Summary: ACCURATE, DENSE, AND ROBUST MULTI-VIEW STEREOPSIS, VOL. 1,NO. 1, AUGUST 2008 1
Accurate, Dense, and Robust Multi-View Stereopsis
Yasutaka Furukawa and Jean Ponce, Fellow, IEEE
Abstract-- This article proposes a novel algorithm for multi-
view stereopsis that outputs a dense set of small rectangular
patches covering the surfaces visible in the images. Stereopsis is
implemented as a match, expand, and filter procedure, starting
from a sparse set of matched keypoints, and repeatedly ex-
panding these before using visibility constraints to filter away
false matches. The keys to the performance of the proposed
algorithm are effective techniques for enforcing local photometric
consistency and global visibility constraints. Simple but effective
methods are also proposed to turn the resulting patch model into
a mesh which can be further refined by an algorithm that enforces
both photometric consistency and regularization constraints. The
proposed approach automatically detects and discards outliers
and obstacles, and does not require any initialization in the form
of a visual hull, a bounding box, or valid depth ranges. We
have tested our algorithm on various datasets including objects
with fine surface details, deep concavities, and thin structures,
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