Optimizing Image Registration for Interactive Applications

详细信息    查看全文

• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9768
• 期：1
• 页码：479-488
• 全文大小：1,877 KB
• 参考文献：1.Alletto, S., Abati, D., Serra, G., Cucchiara, R.: Exploring architectural details through awearable egocentric vision device. Sensors 16(2) (2016)
  2.Arth, C., Wagner, D., Klopschitz, M., Irschara, A., Schmalstieg, D.: Wide area localization on mobile phones. In: Proceedings of IEEE International Symposium on Mixed and Augmented Reality (2009)
  3.Bentley, J.L.: Multidimensional binary search trees used for associative searching. Commun. ACM 18(9), 509–517 (1975)MathSciNet CrossRef MATH
  4.Brown, M., Lowe, D.G.: Unsupervised 3D object recognition and reconstruction in unordered datasets. In: Proceedings of International Conference on 3-D Digital Imaging and Modeling (2005)
  5.Castle, R., Klein, G., Murray, D.W.: Video-rate localization in multiple maps for wearable augmented reality. In: Proceedings of IEEE International Symposium on Wearable Computers (2008)
  6.Hartley, R.I., Zisserman, A.: Multiple View Geometry in Computer Vision, 2nd edn. Cambridge University Press, Cambridge (2004). ISBN: 0521540518
  7.Hauagge, D., Wehrwein, S., Upchurch, P., Bala, K., Snavely, N.: Reasoning about photo collections using models of outdoor illumination. In: Proceedings of British Machine Vision Conference (2014)
  8.Hays, J., Efros, A.: IM2GPS: estimating geographic information from a single image. In: Proceedings of CVPR (2008)
  9.Irschara, A., Zach, C., Frahm, J., Bischof, H.: From structure-from-motion point clouds to fast location recognition. In: Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition (2009)
  10.Jolliffe, I.: Principal Component Analysis. Wiley Online Library (2002)
  11.Ke, Y., Sukthankar, R.: PCA-SIFT: a more distinctive representation for local image descriptors. In: Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition (2004)
  12.Kroeger, T., Van Gool, L.: Video registration to SfM models. In: Proceedings of IEEE European Conference on Computer Vision (2014)
  13.Li, Y., Snavely, N., Huttenlocher, D., Fua, P.: Worldwide pose estimation using 3D point clouds. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part I. LNCS, vol. 7572, pp. 15–29. Springer, Heidelberg (2012)CrossRef
  14.Li, Y., Snavely, N., Huttenlocher, D., Fua, P.: Worldwide pose estimation using 3D point clouds. In: Proceedings of IEEE European Conference on Computer Vision (2012)
  15.Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)CrossRef
  16.Perronnin, F., Sánchez, J., Mensink, T.: Improving the fisher kernel for large-scale image classification. In: Proceedings of IEEE European Conference on Computer Vision (2010)
  17.Pollefeys, M., Van Gool, L., Vergauwen, M., Verbiest, F., Cornelis, K., Tops, J., Koch, R.: Visual modeling with a hand-held camera. Int. J. Comput. Vis. 59(3), 207–232 (2004)CrossRef
  18.Sattler, T., Leibe, B., Kobbelt, L.: Fast image-based localization using direct 2D-to-3D matching. In: Proceedings of IEEE International Conference on Computer Vision (2011)
  19.Sattler, T., Weyand, T., Leibe, B., Kobbelt, L.: Image retrieval for image-based localization revisited. In: Proceedings of British Machine Vision Conference (2012)
  20.Schindler, G., Brown, M., Szeliski, R.: City-scale location recognition. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2007)
  21.Schops, T., Engel, J., Cremers, D.: Semi-dense visual odometry for AR on a smartphone. In: Proceedings of IEEE International Symposium on Mixed and Augmented Reality (2014)
  22.Snavely, N., Seitz, S.M., Szeliski, R.: Photo tourism: exploring photo collections in 3D. ACM Trans. Graph. 25, 835–846 (2006). ACMCrossRef
  23.Snavely, N., Seitz, S.M., Szeliski, R.: Modeling the world from internet photo collections. Int. J. Comput. Vis. 80(2), 189–210 (2008)CrossRef
  24.Torii, A., Arandjelovic, R., Sivic, J., Okutomi, M., Pajdla, T.: 24/7 place recognition by view synthesis. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2015)
  25.Wu, C., Agarwal, S., Curless, B., Seitz, S.: Multicore bundle adjustment. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2011)
  
• 作者单位：Riccardo Gasparini (15)
  Stefano Alletto (15)
  Giuseppe Serra (15)
  Rita Cucchiara (15)
  
  15. University of Modena and Reggio Emilia, Modena, Italy
  
• 丛书名：Augmented Reality, Virtual Reality, and Computer Graphics
• ISBN：978-3-319-40621-3
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349
• 卷排序：9768

文摘

With the spread of wearable and mobile devices, the request for interactive augmented reality applications is in constant growth. Among the different possibilities, we focus on the cultural heritage domain where a key step in the development applications for augmented cultural experiences is to obtain a precise localization of the user, i.e. the 6 degree-of-freedom of the camera acquiring the images used by the application. Current state of the art perform this task by extracting local descriptors from a query and exhaustively matching them to a sparse 3D model of the environment. While this procedure obtains good localization performance, due to the vast search space involved in the retrieval of 2D-3D correspondences this is often not feasible in real-time and interactive environments. In this paper we hence propose to perform descriptor quantization to reduce the search space and employ multiple KD-Trees combined with a principal component analysis dimensionality reduction to enable an efficient search. We experimentally show that our solution can halve the computational requirements of the correspondence search with regard to the state of the art while maintaining similar accuracy levels.