A Method for Registration of 3-D Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
A Flexible New Technique for Camera Calibration
IEEE Transactions on Pattern Analysis and Machine Intelligence
Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models
The Journal of Machine Learning Research
Shape context and chamfer matching in cluttered scenes
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
KinectFusion: Real-time dense surface mapping and tracking
ISMAR '11 Proceedings of the 2011 10th IEEE International Symposium on Mixed and Augmented Reality
Joint Depth and Color Camera Calibration with Distortion Correction
IEEE Transactions on Pattern Analysis and Machine Intelligence
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In this paper we present a unified energy minimization framework for model fitting and pose recovery problems in depth cameras. 3D level-set embedding functions are used to represent object models implicitly and a novel 3D chamfer matching based energy function is minimized by adjusting the generic projection matrix, which could be parameterized differently according to specific applications. Our proposed energy function takes the advantage of the gradient of 3D level-set embedding function and can be efficiently solved by gradients-based optimization methods. We show various real-world applications, including real-time 3D tracking in depth, simultaneous calibration and tracking, and 3D point cloud modeling. We perform experiments on both real data and synthetic data to show the superior performance of our method for all the applications above.