Abstract
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.
This work is funded by REWIRE project under the 7-Framework Programme.
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References
Oikonomidis, I., Kyriazis, N., Argyros, A.: Efficient model-based 3D tracking of hand articulations using kinect. In: Proceedings of the British Machine Vision Conference, pp. 101.1–101.11. BMVA Press (2011)
Newcombe, R.A., Izadi, S., Hilliges, O., Molyneaux, D., Kim, D., Davison, A.J., Kohli, P., Shotton, J., Hodges, S., Fitzgibbon, A.W.: Kinectfusion: Real-time dense surface mapping and tracking. In: ISMAR, pp. 127–136. IEEE (2011)
Koppula, H.S., Anand, A., Joachims, T., Saxena, A.: Semantic labeling of 3D point clouds for indoor scenes. In: NIPS, pp. 244–252 (2011)
Ueda, R.: Tracking 3D objects with point cloud library (2012), pointclouds.org
Besl, P.J., McKay, N.D.: A method for registration of 3-D shapes. IEEE Trans. Pattern Anal. Mach. Intell. 14, 239–256 (1992)
Thayananthan, A., Stenger, B., Torr, P.H.S., Cipolla, R.: Shape context and chamfer matching in cluttered scenes. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, p. 127 (2003)
Daniel Herrera, C., Kannala, J., Heikkila, J.: Joint depth and color camera calibration with distortion correction. IEEE Transactions on Pattern Analysis and Machine Intelligence 99 (2012)
Zhang, Z.: A flexible new technique for camera calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 1330–1334 (2000)
Prisacariu, V., Reid, I.: Simultaneous monocular 2D segmentation, 3D pose recovery and 3D reconstruction. Technical report, University of Oxford (2012)
Lawrence, N.D.: Probabilistic non-linear principal component analysis with gaussian process latent variable models. Journal of Machine Learning Research 6, 1783–1816 (2005)
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Ren, C.Y., Reid, I. (2012). A Unified Energy Minimization Framework for Model Fitting in Depth. In: Fusiello, A., Murino, V., Cucchiara, R. (eds) Computer Vision – ECCV 2012. Workshops and Demonstrations. ECCV 2012. Lecture Notes in Computer Science, vol 7584. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33868-7_8
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DOI: https://doi.org/10.1007/978-3-642-33868-7_8
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