Joint 3D estimation of vehicles and scene flow

ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences

Volumn 2, Issue 3W5, 2015, Pages 427-434

  Menze, M ^a   Heipke, C ^a   Geiger, A ^b  

^a LEIBNIZ UNIVERSITY HANNOVER   (Germany)

^b MAX PLANCK INSTITUTE FOR INTELLIGENT SYSTEMS   (Germany)

Author keywords

3D Reconstruction; Active Shape Model; Motion Estimation; Object Detection; Scene Flow

Indexed keywords

EID: 85014234386     PISSN: 21949042     EISSN: 21949050     Source Type: Conference Proceeding    
DOI: 10.5194/isprsannals-II-3-W5-427-2015     Document Type: Conference Paper

References (38)

1. Bao, S., Chandraker, M., Lin, Y. and Savarese, S., 2013. Dense object reconstruction with semantic priors. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pp. 1264-1271.
2. Basha, T., Moses, Y. and Kiryati, N., 2013. Multi-view scene flow estimation: A view centered variational approach. International Journal of Computer Vision (IJCV) 101(1), pp. 6-21.
3. Bleyer, M., Rhemann, C. and Rother, C., 2012. Extracting 3D scene-consistent object proposals and depth from stereo images. In: Proc. of the European Conf. on Computer Vision (ECCV), pp. 467-481.
4. Bleyer, M., Rother, C., Kohli, P., Scharstein, D. and Sinha, S., 2011. Object stereo-joint stereo matching and object segmentation. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pp. 3081-3088.
5. Cootes, T. F., Taylor, C. J., Cooper, D. H. and Graham, J., 1995.Active shape models-their training and application. Computer Vision and Image Understanding (CVIU) 61(1), pp. 38-59.
6. Dame, A., Prisacariu, V., Ren, C. and Reid, I., 2013. Dense reconstruction using 3D object shape priors. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pp. 1288-1295.
7. Debevec, P. E., Taylor, C. J. and Malik, J., 1996. Modeling and rendering architecture from photographs: A hybrid geometryand image-based approach. In: ACM Trans. on Graphics (SIGGRAPH), pp. 11-20.
8. Geiger, A., Lenz, P. and Urtasun, R., 2012. Are we ready for autonomous driving? The KITTI vision benchmark suite. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pp. 3354-3361.
9. Geiger, A., Ziegler, J. and Stiller, C., 2011. StereoScan: Dense 3D reconstruction in real-time. In: Proc. IEEE Intelligent Vehicles Symposium (IV), pp. 963-968.
10. Guney, F. and Geiger, A., 2015. Displets: Resolving stereo ambiguities using object knowledge. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pp. 4165-4175.
11. Hirschmuller, H., 2008. Stereo processing by semiglobal matching and mutual information. IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI) 30(2), pp. 328-341.
12. Horn, B. K. P. and Schunck, B. G., 1980. Determining optical flow. Artificial Intelligence (AI) 17(1-3), pp. 185-203.
13. Huguet, F. and Devernay, F., 2007. A variational method for scene flow estimation from stereo sequences. In: Proc. IEEE International Conf. on Computer Vision (ICCV), pp. 1-7.
14. Kolmogorov, V., 2006. Convergent tree-reweighted message passing for energy minimization. IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI) 28(10), pp. 1568-1583.
15. Menze, M. and Geiger, A., 2015. Object scene flow for autonomous vehicles. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pp. 3061-3070.
16. Nir, T., Bruckstein, A. M. and Kimmel, R., 2008. Overparameterized variational optical flow. International Journal of Computer Vision (IJCV) 76(2), pp. 205-216.
17. Pacheco, J., Zuffi, S., Black, M. J. and Sudderth, E., 2014. Preserving modes and messages via diverse particle selection. In: Proc. of the International Conf. on Machine learning (ICML), pp. 1152-1160.
18. Pons, J.-P., Keriven, R. and Faugeras, O., 2007. Multi-view stereo reconstruction and scene flow estimation with a global imagebased matching score. International Journal of Computer Vision (IJCV) 72(2), pp. 179-193.
19. Prisacariu, V., Segal, A. and Reid, I., 2013. Simultaneous monocular 2D segmentation, 3D pose recovery and 3D reconstruction. In: Proc. of the Asian Conf. on Computer Vision (ACCV), pp. 593-606.
20. Rother, C., Kolmogorov, V., Lempitsky, V. and Szummer, M., 2007. Optimizing binary MRFs via extended roof duality. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pp. 1-8.
21. Sun, D., Wulff, J., Sudderth, E., Pfister, H. and Black, M., 2013.A fully-connected layered model of foreground and background flow. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pp. 2451-2458.
22. Szeliski, R., 2011. Computer Vision-Algorithms and Applications. Texts in Computer Science, Springer.
23. Trinh, H. and McAllester, D., 2009. Unsupervised learning of stereo vision with monocular cues. In: Proc. of the British Machine Vision Conf. (BMVC), pp. 1-11.
24. Valgaerts, L., Bruhn, A., Zimmer, H., Weickert, J., Stoll, C. and Theobalt, C., 2010. Joint estimation of motion, structure and geometry from stereo sequences. In: Proc. of the European Conf. on Computer Vision (ECCV), pp. 568-581.
25. Vedula, S., Baker, S., Rander, P., Collins, R. and Kanade, T., 1999. Three-dimensional scene flow. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pp. 722-729.
26. Vedula, S., Rander, P., Collins, R. and Kanade, T., 2005. Threedimensional scene flow. IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI) 27(3), pp. 475-480.
27. Vogel, C., Roth, S. and Schindler, K., 2014. View-consistent 3D scene flow estimation over multiple frames. In: Proc. of the European Conf. on Computer Vision (ECCV), pp. 263-278.
28. Vogel, C., Schindler, K. and Roth, S., 2011. 3D scene flow estimation with a rigid motion prior. In: Proc. IEEE International Conf. on Computer Vision (ICCV), pp. 1291-1298.
29. Vogel, C., Schindler, K. and Roth, S., 2013. Piecewise rigid scene flow. In: Proc. IEEE International Conf. on Computer Vision (ICCV), pp. 1377-1384.
30. Wedel, A., Brox, T., Vaudrey, T., Rabe, C., Franke, U. and Cremers, D., 2011. Stereoscopic scene flow computation for 3D motion understanding. International Journal of Computer Vision (IJCV) 95(1), pp. 29-51.
31. Wulff, J. and Black, M. J., 2014. Modeling blurred video with layers. In: Proc. of the European Conf. on Computer Vision (ECCV), pp. 236-252.
32. Yamaguchi, K., McAllester, D. and Urtasun, R., 2013. Robust monocular epipolar flow estimation. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pp. 1862-1869.
33. Yamaguchi, K., McAllester, D. and Urtasun, R., 2014. Efficient joint segmentation, occlusion labeling, stereo and flow estimation. In: Proc. of the European Conf. on Computer Vision (ECCV), pp. 756-771.
34. Zabih, R. and Woodfill, J., 1994. Non-parametric local transforms for computing visual correspondence. In: Proc. of the European Conf. on Computer Vision (ECCV), pp. 151-158.
35. Zia, M., Stark, M. and Schindler, K., 2013a. Explicit occlusion modeling for 3d object class representations. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pp. 3326-3333.
36. Zia, M., Stark, M. and Schindler, K., 2015. Towards scene understanding with detailed 3d object representations. International Journal of Computer Vision (IJCV) 112(2), pp. 188-203.
37. Zia, M., Stark, M., Schiele, B. and Schindler, K., 2011. Revisiting 3d geometric models for accurate object shape and pose. In: Proc. IEEE International Conf. on Computer Vision (ICCV) Workshops, pp. 569-576.
38. Zia, M., Stark, M., Schiele, B. and Schindler, K., 2013b. Detailed 3D representations for object recognition and modeling. IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI) 35(11), pp. 2608-2623.