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;
wherein said dense range data is compared with an estimate of pose to produce an error value and said estimate is iteratively revised to minimize said error and wherein the estimate of pose is generated with reference to a model 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.
40 Citations
20 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; wherein said dense range data is compared with an estimate of pose to produce an error value and said estimate is iteratively revised to minimize said error and wherein the estimate of pose is generated with reference to a model of the figure. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. 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; wherein said dense range data is compared with an estimate of pose to produce an error value and said estimate is iteratively revised to minimize said error and wherein the estimate of pose is generated with reference to a model of the object. - View Dependent Claims (9, 10, 11, 12, 13, 14, 15, 16)
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17. A method for estimating the pose of an articulated object appearing in a sequence of video images, comprising the steps of:
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establishing a model for the surfaces of the articulated object; obtaining dense range data for pixels in each of said video images; shifting a focus of expansion of a point on the object independently by an integer value; generating a hypothetical pose for the object and determining the correlation of the hypothetical pose to the range data for an image; and recursively generating successive hypothetical poses and determining the correlation of each hypothetical pose to identify the pose having the closest correlation to the range data. - View Dependent Claims (18, 19, 20)
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Specification