publication . Conference object . Preprint . 2016

Incremental reconstruction of urban environments by Edge-Points Delaunay triangulation

Matteo Matteucci; Andrea Romanoni;
Open Access
  • Published: 21 Apr 2016
  • Publisher: IEEE
Abstract
Urban reconstruction from a video captured by a surveying vehicle constitutes a core module of automated mapping. When computational power represents a limited resource and, a detailed map is not the primary goal, the reconstruction can be performed incrementally, from a monocular video, carving a 3D Delaunay triangulation of sparse points; this allows online incremental mapping for tasks such as traversability analysis or obstacle avoidance. To exploit the sharp edges of urban landscape, we propose to use a Delaunay triangulation of Edge-Points, which are the 3D points corresponding to image edges. These points constrain the edges of the 3D Delaunay triangulati...
Subjects
ACM Computing Classification System: ComputingMethodologies_COMPUTERGRAPHICSComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONMathematicsofComputing_DISCRETEMATHEMATICS
free text keywords: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Robotics, I.4.5, Computer vision, Iterative reconstruction, Bowyer–Watson algorithm, Computer graphics, Heuristic, Constrained Delaunay triangulation, 3D reconstruction, Artificial intelligence, business.industry, business, Computer science, Obstacle avoidance, Delaunay triangulation
25 references, page 1 of 2

[1] M. Pollefeys, D. Niste´r, J.-M. Frahm, A. Akbarzadeh, P. Mordohai, B. Clipp, C. Engels, D. Gallup, S.-J. Kim, P. Merrell, et al., “Detailed real-time urban 3d reconstruction from video,” International Journal of Computer Vision, vol. 78, no. 2-3, pp. 143-167, 2008.

[2] C. Hane, C. Zach, A. Cohen, R. Angst, and M. Pollefeys, “Joint 3d scene reconstruction and class segmentation,” in Computer Vision and Pattern Recognition (CVPR), 2013 IEEE Conference on. IEEE, 2013, pp. 97-104.

[3] N. Cornelis, B. Leibe, K. Cornelis, and L. Van Gool, “3d urban scene modeling integrating recognition and reconstruction,” International Journal of Computer Vision, vol. 78, no. 2-3, pp. 121-141, 2008.

[4] S. M. Seitz, B. Curless, J. Diebel, D. Scharstein, and R. Szeliski, “A comparison and evaluation of multi-view stereo reconstruction algorithms,” in Computer vision and pattern recognition, 2006 IEEE Computer Society Conference on, vol. 1. IEEE, 2006, pp. 519-528. [OpenAIRE]

[5] N. Snavely, S. M. Seitz, and R. Szeliski, “Photo tourism: exploring photo collections in 3d,” ACM transactions on graphics (TOG), vol. 25, no. 3, pp. 835-846, 2006.

[6] Q. Pan, G. Reitmayr, and T. Drummond, “Proforma: Probabilistic feature-based on-line rapid model acquisition.” in BMVC, 2009, pp. 1-11.

[7] V. Litvinov and M. Lhuillier, “Incremental solid modeling from sparse and omnidirectional structure-from-motion data,” 2013. [OpenAIRE]

[8] D. I. Lovi, N. Birkbeck, D. Cobzas, and M. Jagersand, “Incremental free-space carving for real-time 3d reconstruction,” in Fifth International Symposium on 3D Data Processing Visualization and Transmission(3DPVT), 2010.

[9] V. Litvinov and M. Lhuillier, “Incremental solid modeling from sparse structure-from-motion data with improved visual artifacts removal,” in International Conference on Pattern Recognition (ICPR), 2014. [OpenAIRE]

[10] M. Meyer, M. Desbrun, P. Schro¨der, and A. H. Barr, “Discrete differential-geometry operators for triangulated 2-manifolds,” in Visualization and mathematics III. Springer, 2003, pp. 35-57.

[11] H.-H. Vu, P. Labatut, J.-P. Pons, and R. Keriven, “High accuracy and visibility-consistent dense multiview stereo,” Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 34, no. 5, pp. 889- 901, 2012. [OpenAIRE]

[12] A. Delaunoy, E. Prados, P. Gargallo I Pirace´s, J.-P. Pons, and P. Sturm, “Minimizing the multi-view stereo reprojection error for triangular surface meshes,” in BMVC 2008-British Machine Vision Conference. BMVA, 2008, pp. 1-10.

[13] S. Rhein, G. Lu, S. Sorensen, A. R. Mahoney, H. Eicken, G. C. Ray, and C. Kambhamettu, “Iterative reconstruction of large scenes using heterogeneous feature tracking,” in Computer Vision and Pattern Recognition Workshops (CVPRW), 2013 IEEE Conference on. IEEE, 2013, pp. 407-412.

[14] M. Tomono, “Detailed 3d mapping based on image edge-point icp and recovery from registration failure,” in Intelligent Robots and Systems, 2009. IROS 2009. IEEE/RSJ International Conference on. IEEE, 2009, pp. 1164-1169. [OpenAIRE]

[15] B. D. Lucas and T. Kanade, “An iterative image registration technique with an application to stereo vision.” in IJCAI, vol. 81, 1981, pp. 674-679.

25 references, page 1 of 2
Abstract
Urban reconstruction from a video captured by a surveying vehicle constitutes a core module of automated mapping. When computational power represents a limited resource and, a detailed map is not the primary goal, the reconstruction can be performed incrementally, from a monocular video, carving a 3D Delaunay triangulation of sparse points; this allows online incremental mapping for tasks such as traversability analysis or obstacle avoidance. To exploit the sharp edges of urban landscape, we propose to use a Delaunay triangulation of Edge-Points, which are the 3D points corresponding to image edges. These points constrain the edges of the 3D Delaunay triangulati...
Subjects
ACM Computing Classification System: ComputingMethodologies_COMPUTERGRAPHICSComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONMathematicsofComputing_DISCRETEMATHEMATICS
free text keywords: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Robotics, I.4.5, Computer vision, Iterative reconstruction, Bowyer–Watson algorithm, Computer graphics, Heuristic, Constrained Delaunay triangulation, 3D reconstruction, Artificial intelligence, business.industry, business, Computer science, Obstacle avoidance, Delaunay triangulation
25 references, page 1 of 2

[1] M. Pollefeys, D. Niste´r, J.-M. Frahm, A. Akbarzadeh, P. Mordohai, B. Clipp, C. Engels, D. Gallup, S.-J. Kim, P. Merrell, et al., “Detailed real-time urban 3d reconstruction from video,” International Journal of Computer Vision, vol. 78, no. 2-3, pp. 143-167, 2008.

[2] C. Hane, C. Zach, A. Cohen, R. Angst, and M. Pollefeys, “Joint 3d scene reconstruction and class segmentation,” in Computer Vision and Pattern Recognition (CVPR), 2013 IEEE Conference on. IEEE, 2013, pp. 97-104.

[3] N. Cornelis, B. Leibe, K. Cornelis, and L. Van Gool, “3d urban scene modeling integrating recognition and reconstruction,” International Journal of Computer Vision, vol. 78, no. 2-3, pp. 121-141, 2008.

[4] S. M. Seitz, B. Curless, J. Diebel, D. Scharstein, and R. Szeliski, “A comparison and evaluation of multi-view stereo reconstruction algorithms,” in Computer vision and pattern recognition, 2006 IEEE Computer Society Conference on, vol. 1. IEEE, 2006, pp. 519-528. [OpenAIRE]

[5] N. Snavely, S. M. Seitz, and R. Szeliski, “Photo tourism: exploring photo collections in 3d,” ACM transactions on graphics (TOG), vol. 25, no. 3, pp. 835-846, 2006.

[6] Q. Pan, G. Reitmayr, and T. Drummond, “Proforma: Probabilistic feature-based on-line rapid model acquisition.” in BMVC, 2009, pp. 1-11.

[7] V. Litvinov and M. Lhuillier, “Incremental solid modeling from sparse and omnidirectional structure-from-motion data,” 2013. [OpenAIRE]

[8] D. I. Lovi, N. Birkbeck, D. Cobzas, and M. Jagersand, “Incremental free-space carving for real-time 3d reconstruction,” in Fifth International Symposium on 3D Data Processing Visualization and Transmission(3DPVT), 2010.

[9] V. Litvinov and M. Lhuillier, “Incremental solid modeling from sparse structure-from-motion data with improved visual artifacts removal,” in International Conference on Pattern Recognition (ICPR), 2014. [OpenAIRE]

[10] M. Meyer, M. Desbrun, P. Schro¨der, and A. H. Barr, “Discrete differential-geometry operators for triangulated 2-manifolds,” in Visualization and mathematics III. Springer, 2003, pp. 35-57.

[11] H.-H. Vu, P. Labatut, J.-P. Pons, and R. Keriven, “High accuracy and visibility-consistent dense multiview stereo,” Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 34, no. 5, pp. 889- 901, 2012. [OpenAIRE]

[12] A. Delaunoy, E. Prados, P. Gargallo I Pirace´s, J.-P. Pons, and P. Sturm, “Minimizing the multi-view stereo reprojection error for triangular surface meshes,” in BMVC 2008-British Machine Vision Conference. BMVA, 2008, pp. 1-10.

[13] S. Rhein, G. Lu, S. Sorensen, A. R. Mahoney, H. Eicken, G. C. Ray, and C. Kambhamettu, “Iterative reconstruction of large scenes using heterogeneous feature tracking,” in Computer Vision and Pattern Recognition Workshops (CVPRW), 2013 IEEE Conference on. IEEE, 2013, pp. 407-412.

[14] M. Tomono, “Detailed 3d mapping based on image edge-point icp and recovery from registration failure,” in Intelligent Robots and Systems, 2009. IROS 2009. IEEE/RSJ International Conference on. IEEE, 2009, pp. 1164-1169. [OpenAIRE]

[15] B. D. Lucas and T. Kanade, “An iterative image registration technique with an application to stereo vision.” in IJCAI, vol. 81, 1981, pp. 674-679.

25 references, page 1 of 2
Any information missing or wrong?Report an Issue