Multi-output learning for camera relocalization

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn , Issue , 2014, Pages 1114-1121

  Guzman Rivera, Abner ^b   Kohli, Pushmeet ^a   Glocker, Ben ^c   Shotton, Jamie ^a   Sharp, Toby ^a   Fitzgibbon, Andrew ^a   Izadi, Shahram ^a  

^a MICROSOFT RESEARCH   (United States)

^b UNIVERSITY OF ILLINOIS AT URBANA CHAMPAIGN   (United States)

^c IMPERIAL COLLEGE LONDON   (United Kingdom)

Author keywords

camera relocalization; diverse predictions; Multi output learning

Indexed keywords

3D MODELING; CONVEX OPTIMIZATION; FORECASTING; PATTERN RECOGNITION; RENDERING (COMPUTER GRAPHICS); THREE DIMENSIONAL COMPUTER GRAPHICS;

ACCURATE PREDICTION; DISCRIMINATIVE APPROACH; LEARNING ARCHITECTURES; MULTI-OUTPUT; NONCONVEX OPTIMIZATION; RE-LOCALIZATION; SELECTION PROCEDURES; SIMILARITY FUNCTIONS;

CAMERAS;

EID: 84911380288     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2014.146     Document Type: Conference Paper

References (21)

1. S. Agarwal, N. Snavely, I. Simon, S. M. Seitz, and R. Szeliski. Building Rome in a day. In ICCV, 2009.
2. M. Alexa. Linear combination of transformations. In SIG-GRAPH, 2002.
3. S. Atiya and G. D. Hager. Real-time vision-based robot localization. Robotics and Automation, 1993.
4. D. Batra, P. Yadollahpour, A. Guzman-Rivera, and G. Shakhnarovich. Diverse m-best solutions in markov random fields. In ECCV, 2012.
5. M. A. Brubaker, A. Geiger, and R. Urtasun. Lost! Leveraging the crowd for probabilistic visual self-localization. In CVPR, 2013.
6. E. Bylow, J. Sturm, C. Kerl, F. Kahl, and D. Cremers. Realtime camera tracking and 3D reconstruction using signed distance functions. In RSS, 2013.
7. B. Curless and M. Levoy. A volumetric method for building complex models from range images. In SIGGRAPH, 1996.
8. A. Davison, I. Reid, N. Molton, and O. Stasse. MonoSLAM: Real-time single camera SLAM. PAMI, 2007.
9. O. Dekel and O. Shamir. There's a hole in my data space: Piecewise predictors for heterogeneous learning problems. In AISTATS, 2012.
10. G. N. DeSouza and A. C. Kak. Vision for mobile robot navigation: a survey. PAMI, 24(2):237-267, 2002.
11. Q. Gu and J. Han. Clustered Support Vector Machines. In AISTATS, 2013.
12. A. Guzman-Rivera, D. Batra, and P. Kohli. Multiple choice learning: Learning to produce multiple structured outputs. In Proc. NIPS, 2012.
13. G. Klein and D. Murray. Improving the agility of keyframe-based SLAM. In ECCV, 2008.
14. A. Kulesza and B. Taskar. Determinantal point processes for machine learning. Foundations and Trends in Machine Learning, 5(2-3), 2012.
15. V. Lepetit and P. Fua. Keypoint recognition using randomized trees. PAMI, 28(9), 2006.
16. R. Newcombe, S. Izadi, O. Hilliges, D. Molyneaux, D. Kim, A. Davison, P. Kohli, J. Shotton, S. Hodges, and A. Fitzgibbon. KinectFusion: Real-time dense surface mapping and tracking. In Proc. ISMAR, 2011.
17. R. Salas-Moreno, R. Newcombe, H. Strasdat, P. Kelly, and A. Davison. SLAM++: Simultaneous localisation and mapping at the level of objects. In CVPR, 2013.
18. S. Se, D. G Lowe, and J. J. Little. Vision-based global localization and mapping for mobile robots. Robotics, 2005.
19. J. Shotton, B. Glocker, C. Zach, S. Izadi, A. Criminisi, and A. Fitzgibbon. Scene coordinate regression forests for camera relocalization in RGB-D images. In CVPR, 2013.
20. B. Williams, G. Klein, and I. Reid. Automatic relocalization and loop closing for real-time monocular SLAM. PAMI, 33(9):1699-1712, 2011.
21. Y. Yang and D. Ramanan. Articulated pose estimation with flexible mixtures-of-parts. In CVPR, 2011.