Camera pose estimation based on global structure from motion

In this paper, a new global camera pose estimation algorithm WTLS-IRLS is proposed, which can effectively solve the global rotation when there are outliers. Firstly, according to the relationship between the rotation vector and the rotation matrix, we simplify the product operation of the rotation matrix into the subtraction operation of the rotation vector, which reduces the complexity of the algorithm. Secondly, the weighted total least squares (WTLS) and the iteratively reweighted least squares (IRLS) are used to average relative rotations. As the initialization of IRLS, WTLS provides a good initial guess by correcting the linearization equation and adding weight information to the relative rotations. IRLS continues to add weight information to the relative rotation matrices to optimize the global rotations. We demonstrate the performance of our approach by a number of large-scale data sets, the results show that our method has been greatly improved in efficiency, accuracy and iteration. In order to verify the correctness of our proposed method, we completed the complete reconstruction process, the experimental results show that our proposed WTLS-IRLS rotation averaging algorithm can obtain dense point clouds with more three-dimensional points.

[1]  B. Rossi,et al.  Robust Absolute Rotation Estimation via Low-Rank and Sparse Matrix Decomposition , 2014, 2014 2nd International Conference on 3D Vision.

[2]  Jochen Trumpf,et al.  Generalized Weiszfeld Algorithms for Lq Optimization , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Venu Madhav Govindu,et al.  Robustness in Motion Averaging , 2006, ACCV.

[4]  Hongdong Li,et al.  Five-Point Motion Estimation Made Easy , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[5]  Changchang Wu,et al.  Towards Linear-Time Incremental Structure from Motion , 2013, 2013 International Conference on 3D Vision.

[6]  Leonidas J. Guibas,et al.  Learning Transformation Synchronization , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[7]  Richard Szeliski,et al.  Structure from motion for scenes with large duplicate structures , 2011, CVPR 2011.

[8]  Venu Madhav Govindu,et al.  Robust Relative Rotation Averaging , 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Marc Pollefeys,et al.  Disambiguating visual relations using loop constraints , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[10]  Xin Li,et al.  Simplified mirror-based camera pose computation via rotation averaging , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[11]  Noah Snavely,et al.  Robust Global Translations with 1DSfM , 2014, ECCV.

[12]  Venu Madhav Govindu,et al.  Lie-algebraic averaging for globally consistent motion estimation , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[13]  Zhanyi Hu,et al.  Robust global translation averaging with feature tracks , 2016, 2016 23rd International Conference on Pattern Recognition (ICPR).

[14]  Steven M. Seitz,et al.  Multicore bundle adjustment , 2011, CVPR 2011.

[15]  Andrew Owens,et al.  Discrete-continuous optimization for large-scale structure from motion , 2011, CVPR 2011.

[16]  Ping Tan,et al.  Global Structure-from-Motion by Similarity Averaging , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[17]  Rui Guo,et al.  Weighted motion averaging for the registration of multi-view range scans , 2017, Multimedia Tools and Applications.

[18]  Venu Madhav Govindu,et al.  Efficient and Robust Large-Scale Rotation Averaging , 2013, 2013 IEEE International Conference on Computer Vision.

[19]  Richard Szeliski,et al.  Towards Internet-scale multi-view stereo , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[20]  René Vidal,et al.  Distributed computer vision algorithms through distributed averaging , 2011, CVPR 2011.

[21]  Andrew Zisserman,et al.  Robust parameterization and computation of the trifocal tensor , 1997, Image Vis. Comput..

[22]  Wei Jia,et al.  Robust bundle adjustment for large-scale structure from motion , 2017, Multimedia Tools and Applications.

[23]  Jochen Trumpf,et al.  L1 rotation averaging using the Weiszfeld algorithm , 2011, CVPR 2011.

[24]  Xin Wang,et al.  Structure from motion for ordered and unordered image sets based on random k-d forests and global pose estimation , 2019, ISPRS Journal of Photogrammetry and Remote Sensing.

[25]  Andrea Fusiello,et al.  Hierarchical structure-and-motion recovery from uncalibrated images , 2015, Comput. Vis. Image Underst..

[26]  Youping Chen,et al.  Globally Optimal Estimates for Rotation Averaging Problems , 2014, 2014 Sixth International Conference on Intelligent Human-Machine Systems and Cybernetics.

[27]  Richard Szeliski,et al.  Modeling the World from Internet Photo Collections , 2008, International Journal of Computer Vision.

[28]  David Nistér,et al.  An efficient solution to the five-point relative pose problem , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[29]  Venu Madhav Govindu,et al.  Combining two-view constraints for motion estimation , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[30]  Andreas Terzis,et al.  Distributed pose averaging in camera networks via consensus on SE(3) , 2008, 2008 Second ACM/IEEE International Conference on Distributed Smart Cameras.