Three dimensional object pose estimation which employs dense depth information
First Claim
1. A method for estimating the pose of an articulated figure, comprising the steps of:
- obtaining dense range data which describes the distance of points on the figure from a reference;
shifting a focus of expansion of a point on the figure independently by an integer value; and
processing said dense range data to estimate the pose of the figure.
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Abstract
Dense range data obtained at real-time rates is employed to estimate the pose of an articulated figure. In one approach, the range data is used in combination with a model of connected patches. Each patch is the planar convex hull of two circles, and a recursive procedure is carried out to determine an estimate of pose which most closely correlates to the range data. In another aspect of the invention, the dense range data is used in conjunction with image intensity information to improve pose tracking performance. The range information is used to determine the shape of an object, rather than assume a generic model or estimate structure from motion. In this aspect of the invention, a depth constraint equation, which is a counterpart to the classic brightness change constraint equation, is employed. Both constraints are used to jointly solve for motion estimates.
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Citations
21 Claims
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1. A method for estimating the pose of an articulated figure, comprising the steps of:
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obtaining dense range data which describes the distance of points on the figure from a reference; shifting a focus of expansion of a point on the figure independently by an integer value; and processing said dense range data to estimate the pose of the figure. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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9. A method for estimating the pose of an object, comprising the steps of:
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obtaining dense range data which describes the distance of points on the object from a reference; shifting a focus of expansion of a point on the object independently by an integer value; and processing said dense range data in accordance with a set of linear depth constraints to estimate the pose of the object. - View Dependent Claims (10, 11, 12, 13, 14, 15, 16, 17, 18)
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19. A method for estimating the pose of an object appearing in a sequence of video images, comprising the steps of:
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obtaining dense brightness data for pixels in each of said video images; obtaining dense range data for pixels in each of said video images; determining an initial pose for the object in one of said video images; and estimating changes in at least one of the translational position and rotational orientation of the object for successive images, on the basis of said brightness data and said range data, shifting a focus of expansion of a point on the object independently by an integer to thereby estimate the pose of the object in successive images. - View Dependent Claims (20, 21)
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Specification