Abstract
One of the potentially effective means for 3D reconstruction is to reconstruct the scene in a global manner, rather than incrementally, by fully exploiting available auxiliary information on imaging condition, such as camera location by GPS, orientation by IMU(or Compass), focal length from EXIF etc. However these auxiliary information, though informative and valuable, is usually too noisy to be directly usable. In this paper, we present a global method by taking advantage of such noisy auxiliary information to improve SfM solving. More specifically, we introduce two effective iterative optimization algorithms directly initiated with such noisy auxiliary information. One is a robust iterative rotation estimation algorithm to deal with contaminated EG(epipolar graph), the other is a robust iterative scene reconstruction algorithm to deal with noisy GPS data for camera centers initialization. We found that by exclusively focusing on the inliers estimated at the current iteration, called potential inliers in this work, the optimization process initialized by such noisy auxiliary information could converge well and efficiently. Our proposed method is evaluated on real images captured by UAV(unmanned aerial vehicle), StreetView car and conventional digital cameras. Extensive experimental results show that our method performs similarly or better than many of the state-of-art reconstruction approaches, in terms of reconstruction accuracy and scene completeness, but more efficient and scalable for large-scale image datasets.
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References
Crandall, D., Owens, A., Snavely, N., Huttenlocher, D.: SfM with MRFs: discrete-continuous optimization for large-scale structure from motion. PAMI 35, 2841–2853 (2013)
Irschara, A., Hoppe, C., Bischof, H., Kluckner, S.: Efficient structure from motion with weak position and orientation priors. In: CVPRW, pp. 21–28. IEEE (2011)
Snavely, N., Seitz, S.M., Szeliski, R.: Modeling the world from internet photo collections. IJCV 80, 189–210 (2008)
Agarwal, S., Furukawa, Y., Snavely, N., Simon, I., Curless, B., Seitz, S.M., Szeliski, R.: Building rome in a day. ACM 54, 105–112 (2011)
Moulon, P., Monasse, P., Marlet, R.: Adaptive structure from motion with a contrario model estimation. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds.) ACCV 2012, Part IV. LNCS, vol. 7727, pp. 257–270. Springer, Heidelberg (2013)
Furukawa, Y., Ponce, J.: Accurate, dense, and robust multiview stereopsis. PAMI 32, 1362–1376 (2010)
Strecha, C., Pylvanainen, T., Fua, P.: Dynamic and scalable large scale image reconstruction. In: CVPR, pp. 406–413. IEEE (2010)
Wu, C.: Towards linear-time incremental structure from motion. In: International Conference on 3D Vision, pp. 127–134 (2013)
Moulon, P., Monasse, P., Marlet, R.: Global fusion of relative motionsfor robust, accurate and scalable structure from motion. In: ICCV (2013)
Nianjuan Jiang, Zhaopeng Cui, P.T.: A global linear method for camera pose registration. In: ICCV (2013)
Haner, S., Heyden, A.: Covariance propagation and next best view planning for 3D reconstruction. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part II. LNCS, vol. 7573, pp. 545–556. Springer, Heidelberg (2012)
Snavely, N., Seitz, S.M., Szeliski, R.: Skeletal graphs for efficient structure from motion. In: CVPR (2008)
Lou, Y., Snavely, N., Gehrke, J.: MatchMiner: efficient spanning structure mining in large image collections. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part II. LNCS, vol. 7573, pp. 45–58. Springer, Heidelberg (2012)
Nister, D., Stewenius, H.: Scalable recognition with a vocabulary tree. In: CVPR (2006)
Chum, O., Mikulik, A., Perdoch, M., Matas, J.: Total recall ii: query expansion revisited. In: CVPR (2011)
Jian, C., Cong, L., Jiaxiang, W., Hainan, C., Hanqing, L.: Fast and accurate image matching with cascade hashing for 3d reconstruction. In: CVPR (2014)
Carceroni, R., Kumar, A., Daniilidis, K.: Structure from motion with known camera positions. In: CVPR (2006)
Pollefeys, M., Nistér, D., Frahm, J.M., Akbarzadeh, A., Mordohai, P., et al.: Detailed real-time urban 3d reconstruction from video. IJCV 78, 143–167 (2008)
Sinha, S.N., Steedly, D., Szeliski, R.: A multi-stage linear approach to structure from motion. In: Kutulakos, K.N. (ed.) ECCV 2010 Workshops, Part II. LNCS, vol. 6554, pp. 267–281. Springer, Heidelberg (2012)
Nistér, D.: An efficient solution to the five-point relative pose problem. PAMI 26, 756–770 (2004)
Tardif, J.P.: Non-iterative approach for fast and accurate vanishing point detection. In: ICCV (2009)
Martinec, D., Pajdla, T.: Robust rotation and translation estimation in multiview reconstruction. In: CVPR (2007)
Agarwal, S., Mierle, K., Others: Ceres solver. http://ceres-solver.org/
Moulon, P.: openMVG. https://github.com/openMVG/openMVG/tree/master
Wu, C.: Critical configurations for radial distortion self-calibration. In: CVPR (2014)
Acknowledgement
This work was supported by National High Technology \( R \& D\) Program of China(863 program) under the grant No \(2013AA12A202\) and NSFC under the grant No \((61333015, 61273280)\).
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Cui, H., Shen, S., Gao, W., Hu, Z. (2015). Fusion of Auxiliary Imaging Information for Robust, Scalable and Fast 3D Reconstruction. In: Cremers, D., Reid, I., Saito, H., Yang, MH. (eds) Computer Vision – ACCV 2014. ACCV 2014. Lecture Notes in Computer Science(), vol 9003. Springer, Cham. https://doi.org/10.1007/978-3-319-16865-4_15
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DOI: https://doi.org/10.1007/978-3-319-16865-4_15
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