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2003
IEEE

Pose Estimation using 3D View-Based Eigenspaces

9 years 11 months ago
Pose Estimation using 3D View-Based Eigenspaces
In this paper we present a method for estimating the absolute pose of a rigid object based on intensity and depth viewbased eigenspaces, built across multiple views of example objects of the same class. Given an initial frame of an object with unknown pose, we reconstruct a prior model for all views represented in the eigenspaces. For each new frame, we compute the pose-changes between every view of the reconstructed prior model and the new frame. The resulting pose-changes are then combined and used in a Kalman filter update. This approach for pose estimation is userindependent and the prior model can be initialized automatically from any view point of the view-based eigenspaces. To track more robustly over time, we present an extension of this pose estimation technique where we integrate our prior model approach with an adaptive differential tracker. We demonstrate the accuracy of our approach on face pose tracking using stereo cameras.
Louis-Philippe Morency, Patrik Sundberg, Trevor Da
Added 04 Jul 2010
Updated 04 Jul 2010
Type Conference
Year 2003
Where AMFG
Authors Louis-Philippe Morency, Patrik Sundberg, Trevor Darrell
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