NDLI: Dense specular shape from multiple specular flows

Content Provider	IEEE Xplore Digital Library
Author	Vasilyev, Y. Adato, Y. Zickler, T. Ben-Shahar, O.
Copyright Year	2008
Description	Author affiliation: Harvard Sch. of Eng. & Appl. Sci., Cambridge, MA (Vasilyev, Y.)
Abstract	The inference of specular (mirror-like) shape is a particularly difficult problem because an image of a specular object is nothing but a distortion of the surrounding environment. Consequently, when the environment is unknown, such an image would seem to convey little information about the shape itself. It has recently been suggested (Adato et al., ICCV 2007) that observations of relative motion between a specular object and its environment can dramatically simplify the inference problem and allow one to recover shape without explicit knowledge of the environment content. However, this approach requires solving a non-linear PDE (the dasiashape from specular flow equationpsila) and analytic solutions are only known to exist for very constrained motions. In this paper, we consider the recovery of shape from specular flow under general motions. We show that while the dasiashape from specular flowpsila PDE for a single motion is non-linear, we can combine observations of multiple specular flows from distinct relative motions to yield a linear set of equations. We derive necessary conditions for this procedure, discuss several numerical issues with their solution, and validate our results quantitatively using image data.
Starting Page	1
Ending Page	8
File Size	980547
Page Count	8
File Format	PDF
ISBN	9781424422425
ISSN	10636919
DOI	10.1109/CVPR.2008.4587685
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-06-23
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Shape Nonlinear equations Motion analysis Nonlinear distortion Optical reflection Surface reconstruction Image reconstruction Computer science Art Mirrors
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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