Abstract
We present a probabilistic, online, depth map fusion framework, whose generative model for the sensor measurement process accurately incorporates both long-range visibility constraints and a spatially varying, probabilistic outlier model. In addition, we propose an inference algorithm that updates the state variables of this model in linear time each frame. Our detailed evaluation compares our approach against several others, demonstrating and explaining the improvements that this model offers, as well as highlighting a problem with all current methods: systemic bias.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Szeliski, R.: A multi-view approach to motion and stereo. In: Proceedings of CVPR (1999)
Kolmogorov, V., Zabih, R.: Computing visual correspondence with occlusions using graph cuts. In: Proceedings of ICCV (2001)
Merrell, P., Akbarzadeh, A., Wang, L., Mordohai, P., Frahm, J.M., Yang, R., Nistér, D., Pollefeys, M.: Real-time visibility-based fusion of depth maps. In: Proceedings of ICCV (2007)
Seitz, M.S., Dyer, C.R.: Photorealistic scene reconstruction by voxel coloring. In: Proceedings of CVPR (1997)
Hernández, C., Vogiatzis, G., Cipolla, R.: Probabilistic visibility for multi-view stereo. In: Proceedings of CVPR (2007)
Liu, S., Cooper, D.B.: A complete statistical inverse ray tracing approach to multi-view stereo. In: Proceedings of CVPR, pp. 913–920 (2011)
Elfes, A., Matthies, L.: Sensor integration for robot navigation: Combining sonar and stereo range data in a grid-based representation. In: Proceedings of IEEE Conference on Decision and Control (1987)
Konolige, K.: Improved occupancy grids for map building. Autonomous Robots 4, 351–367 (1997)
Collins, T., Collins, J.J., Ryan, C.: Occupancy grid mapping: An empirical evaluation. In: Proceedings of the Mediterranean Conference on Control Automation (2007)
Guan, L., Franco, J.S., Pollefeys, M.: 3D object reconstruction with heterogeneous sensor data. In: Proceedings of 3DPVT (2008)
Kim, Y.M., Theobalt, C., Diebel, J., Kosecka, J., Miscusik, B., Thrun, S.: Multi-view image and ToF sensor fusion for dense 3D reconstruction. In: Proceedings of ICCV Workshops (2009)
Pathak, K., Birk, A., Poppinga, J., Schwertfeger, S.: 3D forward sensor modeling and application to grid based sensor fusion. In: Proceedings of IROS (2007)
Thrun, S.: Learning occupancy grids with forward models. Autonomous Robots 15, 111–127 (2001)
Curless, B., Levoy, M.: A volumetric method for building complex models from range images. In: Proceedings of SIGGRAPH (1996)
Newcombe, R.A., Izadi, S., Hiliges, O., Molyneaux, D., Kim, D., Davison, A.J., Kohli, P., Shotton, J., Hodges, S., Fitzgibbon, A.: KinectFusion: Real-time dense surface mapping and tracking. In: Proceedings of ISMAR (2011)
Zach, C., Pock, T., Bischof, H.: A globally optimal algorithm for robust TV-L \(^\textrm{1}\) range image integration. In: Proceedings of ICCV (2007)
Vogiatzis, G., Hernández, C.: Video-based, real-time multi view stereo. Image and Vision Computing 29(7), 434–441 (2011)
Bishop, C.M.: Pattern Recognition and Machine Learning. Springer (2006)
Minka, T.: A family of algorithms for approximate Bayesian inference. PhD thesis. MIT (2001)
Point Cloud Library, http://pointclouds.org
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Woodford, O.J., Vogiatzis, G. (2012). A Generative Model for Online Depth Fusion. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds) Computer Vision – ECCV 2012. ECCV 2012. Lecture Notes in Computer Science, vol 7576. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33715-4_11
Download citation
DOI: https://doi.org/10.1007/978-3-642-33715-4_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33714-7
Online ISBN: 978-3-642-33715-4
eBook Packages: Computer ScienceComputer Science (R0)