Abstract
In this paper, we propose a novel method for generating 3D line segment based model from an image sequence taken with a RGB-D camera. Constructing 3D geometrical representation by 3D model is essential for model based camera pose estimation that can be performed by corresponding 2D features in images with 3D features of the captured scene. While point features are mostly used as such features for conventional camera pose estimation, we aim to use line segment features for improving the performance of the camera pose estimation. In this method, using RGB images and depth images of two continuous frames, 2D line segments from the current frame and 3D line segments from the previous frame are corresponded. The 2D-3D line segment correspondences provide camera pose of the current frame. All of 2D line segments are finally back-projected to the world coordinate based on the estimated camera pose for generating 3D line segment based model of the target scene. In experiments, we confirmed that the proposed method can successfully generate line segment based models, while 3D models based on the point features often fail to successfully represent the target scene.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Sturm, P., Triggs, B.: A factorization based algorithm for multi-image projective structure and motion. In: Buxton, B.F., Cipolla, R. (eds.) ECCV 1996. LNCS, vol. 1065. Springer, Heidelberg (1996)
Lowe, D.G.: Object recognition from local scale-invariant features. In: Proceedings of the Seventh IEEE International Conference on Computer Vision, vol. 2, pp. 1150–1157. IEEE (1999)
Bay, H., Tuytelaars, T., Van Gool, L.: SURF: speeded up robust features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006, Part I. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006)
Jain, A., Kurz, C., Thormahlen, T., Seidel, H.P.: Exploiting global connectivity constraints for reconstruction of 3d line segments from images. In: IEEE Conference on Computer Vision and Pattern Recognition, IEEE, pp. 1586–1593 (2010)
Hofer, M., Wendel, A., Bischof, H.: Line-based 3d reconstruction of wiry objects. In: Proceedings of the 18th Computer Vision Winter Workshop (2013)
Hofer, M., Wendel, A., Bischof, H.: Incremental line-based 3d reconstruction using geometric constraints (2013)
Wang, Z., Wu, F., Hu, Z.: MSLD: a robust descriptor for line matching. Pattern Recogn. 42, 941–953 (2009)
Hirose, K., Saito, H.: Fast line description for line-based slam. In: Proceedings of the British Machine Vision Conference, pp. 83.1–83.11 (2012)
Nakayama, Y., Honda, T., Saito, H., Shimizu, M., Yamaguchi, N.: Accurate camera pose estimation for kinectfusion based on line segment matching by LEHF. In: Proceedings of the International Conference on Pattern Recognition, pp. 2149–2154 (2014)
Von Gioi, R.G., Jakubowicz, J., Morel, J.M., Randall, G.: LSD: a fast line segment detector with a false detection control. IEEE Trans. Pattern Anal. Mach. Intell. 32, 722–732 (2010)
Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24, 381–395 (1981)
Fan, B., Wu, F., Hu, Z.: Line matching leveraged by point correspondences. In: IEEE Conference on Computer Vision and Pattern Recognition, IEEE, pp. 390–397 (2010)
Zhang, L., Xu, C., Lee, K.-M., Koch, R.: Robust and efficient pose estimation from line correspondences. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds.) ACCV 2012, Part III. LNCS, vol. 7726, pp. 217–230. Springer, Heidelberg (2013)
Kumar, R., Hanson, A.R.: Robust methods for estimating pose and a sensitivity analysis. CVGIP Image Underst. 60, 313–342 (1994)
Autodesk 123D Catch. http://www.123dapp.com/catch
Newcombe, R.A., Davison, A.J., Izadi, S., Kohli, P., Hilliges, O., Shotton, J., Molyneaux, D., Hodges, S., Kim, D., Fitzgibbon, A.: Kinectfusion: real-time dense surface mapping and tracking. In: Proceedings of the 10th IEEE International Symposium on Mixed and Augmented Reality, IEEE, pp. 127–136 (2011)
Rusu, R.B., Cousins, S.: 3d is here: point cloud library (PCL). In: Proceedings of IEEE International Conference on Robotics and Automation, IEEE, pp. 1–4 (2011)
Besl, P.J., McKay, N.D.: Method for registration of 3-d shapes. In: Proceedings of SPIE 1611, Sensor Fusion IV: Control Paradigms and Data Structures, International Society for Optics and Photonics, pp. 586–606 (1992)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Nakayama, Y., Saito, H., Shimizu, M., Yamaguchi, N. (2015). 3D Line Segment Based Model Generation by RGB-D Camera for Camera Pose Estimation. In: Jawahar, C., Shan, S. (eds) Computer Vision - ACCV 2014 Workshops. ACCV 2014. Lecture Notes in Computer Science(), vol 9010. Springer, Cham. https://doi.org/10.1007/978-3-319-16634-6_34
Download citation
DOI: https://doi.org/10.1007/978-3-319-16634-6_34
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-16633-9
Online ISBN: 978-3-319-16634-6
eBook Packages: Computer ScienceComputer Science (R0)