Abstract
In this paper we present a probabilistic algorithm for multi-view reconstruction from calibrated images. The algorithm is based on multi-resolution volumetric range image integration and is highly separable as it only employs local optimization. Dense depth maps are transformed in an octree data structure with variable voxel sizes. This allows for an efficient modeling of point clouds with very variable density. A probability function constructed in discrete space is built locally with a Bayesian approach. Compared to other algorithms we can deal with extremely big scenes and complex camera configurations in a limited amount of time, as the solution can be split in arbitrarily many parts and computed in parallel. The algorithm has been applied to lab and outdoor benchmark data as well as to large image sets of urban regions taken by cameras on Unmanned Aerial Vehicles (UAVs) and from the ground, demonstrating high surface quality and good runtime performance.
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References
Bodenmüller, T.: Streaming Surface Reconstruction from Real Time 3D Measurements. Ph.D. thesis, Technical University Munich (2009)
Campbell, N.D., Vogiatzis, G., Hernández, C., Cipolla, R.: Using multiple hypotheses to improve depth-maps for multi-view stereo. In: CVPR (2008)
Curless, B., Levoy, M.: A volumetric method for building complex models from range images. In: SIGGRAPH (1996)
Fuhrmann, S., Goesele, M.: Fusion of depth maps with multiple scales. In: Proceedings of the 2011 SIGGRAPH Asia Conference (2011)
Furukawa, Y., Ponce, J.: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
Gallup, D., Frahm, J.M., Mordohai, P., Yang, Q., Pollefeys, M.: Real-time plane-sweeping stereo with multiple sweeping directions. In: CVPR (2007)
Gallup, D., Pollefeys, M., Frahm, J.-M.: 3D reconstruction using an n-layer heightmap. In: Goesele, M., Roth, S., Kuijper, A., Schiele, B., Schindler, K. (eds.) Pattern Recognition. LNCS, vol. 6376, pp. 1–10. Springer, Heidelberg (2010)
Goesele, M., Curless, B., Seitz, S.M.: Multi-view stereo revisited. In: CVPR (2006)
Guan, L., Franco, J.S., Pollefeys, M.: 3D Object Reconstruction with Heterogeneous Sensor Data. In: 3DPVT (2008)
Hirschmüller, H.: Stereo Vision based mapping and immediate virtual walkthroughs. Ph.D. thesis, De Montfort University (2003)
Hirschmüller, H.: Stereo processing by semiglobal matching and mutual information. PAMI (2008)
Hirschmüller, H., Scharstein, D.: Evaluation of stereo matching costs on images with radiometric differences. PAMI (2009)
Jancosek, M., Shekhovtsov, A., Pajdla, T.: Scalable multi-view stereo. In: 3DIM09 (2009)
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: CVPR (2007)
Mücke, P., Klowsky, R., Goesele, M.: Surface reconstruction from multi-resolution sample points. In: VMV (2011)
Seitz, S.M., Curless, B., Diebel, J., Scharstein, D., Szeliski, R.: A comparison and evaluation of multi-view stereo reconstruction algorithms. In: CVPR (2006)
Strecha, C., Fransens, R., Gool, L.J.V.: Combined depth and outlier estimation in multi-view stereo. In: CVPR (2006)
Strecha, C., von Hansen, W., Gool, L.J.V., Fua, P., Thoennessen, U.: On benchmarking camera calibration and multi-view stereo for high resolution imagery. In: CVPR (2008)
Thrun, S., Burgard, W., Fox, D.: Probabilistic Robotics (Intelligent Robotics and Autonomous Agents). MIT Press (2005)
Vogiatzis, G., Hernández, C.: Video-based, real-time multi-view stereo. Image Vision Comput (2011)
Vu, H.H., Labatut, P., Pons, J.P., Keriven, R.: High accuracy and visibility-consistent dense multiview stereo. PAMI (2012)
Woodford, O.J., Vogiatzis, G.: A generative model for online depth fusion. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part V. LNCS, vol. 7576, pp. 144–157. Springer, Heidelberg (2012)
Zach, C.: Fast and high quality fusion of depth maps. In: 3DPVT (2008)
Zach, C., Pock, T., Bischof, H.: A globally optimal algorithm for robust tv-l1 range image integration. In: ICCV (2007)
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Kuhn, A., Hirschmüller, H., Mayer, H. (2013). Multi-Resolution Range Data Fusion for Multi-View Stereo Reconstruction. In: Weickert, J., Hein, M., Schiele, B. (eds) Pattern Recognition. GCPR 2013. Lecture Notes in Computer Science, vol 8142. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40602-7_5
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DOI: https://doi.org/10.1007/978-3-642-40602-7_5
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