Least-Squares Estimation of Transformation Parameters Between Two Point Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Three-dimensional computer vision: a geometric viewpoint
Three-dimensional computer vision: a geometric viewpoint
Model-based object pose in 25 lines of code
International Journal of Computer Vision - Special issue: image understanding research at the University of Maryland
Uniform Distribution, Distance and Expectation Problems for Geometric Features Processing
Journal of Mathematical Imaging and Vision
Journal of Mathematical Imaging and Vision
Optimization Criteria and Geometric Algorithms for Motion and Structure Estimation
International Journal of Computer Vision
Means and Averaging in the Group of Rotations
SIAM Journal on Matrix Analysis and Applications
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
Distributed optimization in sensor networks
Proceedings of the 3rd international symposium on Information processing in sensor networks
SoftPOSIT: Simultaneous Pose and Correspondence Determination
International Journal of Computer Vision
A scheme for robust distributed sensor fusion based on average consensus
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
Distributed metric calibration of ad hoc camera networks
ACM Transactions on Sensor Networks (TOSN)
Calibrating distributed camera networks using belief propagation
EURASIP Journal on Applied Signal Processing
On Spatial Gossip Algorithms for Average Consensus
SSP '07 Proceedings of the 2007 IEEE/SP 14th Workshop on Statistical Signal Processing
Consensus Optimization on Manifolds
SIAM Journal on Control and Optimization
Distributed Kalman filtering based on consensus strategies
IEEE Journal on Selected Areas in Communications
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Our work addresses pose estimation in a distributed camera framework. We examine how processing cameras can best reach a consensus about the pose of an object when they are each given a model of the object, defined by a set of point coordinates in the object frame of reference. The cameras can only see a subset of the object feature points in the midst of background clutter points, not knowing which image points match with which object points, nor which points are object points or background points. The cameras individually recover a prediction of the object's pose using their knowledge of the model, and then exchange information with their neighbors, performing consensus updates locally to obtain a single estimate consistent across all cameras, without requiring a common centralized processor. Our main contributions are: 1) we present a novel algorithm performing consensus updates in 3-D world coordinates penalized by a 3-D model, and 2) we perform a thorough comparison of our method with other current consensus methods. Our method is consistently the most accurate, and we confirm that the existing consensus method based upon calculating the Karcher mean of rotations is also reliable and fast. Experiments on simulated and real imagery are reported.