Abstract
In this paper, we propose a new method for 3D-2D registration based on SIFT and a range intensity image, which is a kind of intensity image simultaneously acquired with a range image using an active range sensor. A linear equation for the registration parameters is formulated, which is combined with displacement estimations for extrinsic and intrinsic parameters and the distortion of a camera’s lens. This equation is solved to match a range intensity image and a color image using SIFT. The range intensity and color images differ, and the pairs of matched feature points usually contain a number of false matches. To reduce false matches, a range intensity image is combined with the background image of a color image. Then, a range intensity image is corrected for extracting good candidates. Moreover, to remove false matches while keeping correct matches, soft matching, in which false matches are weakly removed, is used. First, false matches are removed by using scale information from SIFT. Secondly, matching reliability is defined from the Bhattacharyya distance of the pair of matched feature points. Then RANSAC is applied. In this stage, its threshold is kept high. In our approach, the accuracy of registration is advanced. The effectiveness of the proposed method is illustrated by experiments with real-world objects.
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
Preview
Unable to display preview. Download preview PDF.
References
Levoy, M., Pulli, K., Curless, B., Rusinkiewicz, S., Koller, D., Pereira, L., Ginzton, M., Anderson, S., Davis, J., Ginsberg, J., Shade, J., Fulk, D.: The digital Michelangelo project:3D scanning of large statues. In: SIGGRAPH 2000, pp. 131–144 (2000)
Iwakiri, Y., Kaneko, T.: PC-based realtime texture painting on real world objects. In: Proc. Eurographics 2001, vol. 20, pp. 105–113 (2001)
Lensch, H.P.A., Heidrich, W., Seidel, H.P.: Automated texture registration and stitching for real world models. In: Proc. Pacific Graphics 2000, pp. 317–326 (2000)
Lavallee, S., Szeliski, R.: Recovering the position and orientation of free -form objects from image contours using 3D distance maps. IEEE Trans. Pattern Anal. Mach. Intell. 17(4), 378–390 (1995)
Neugebauer, P.J., Klein, K.: Texturing 3D models of real world objects from multiple unregistered photographic views. In: Proc. Eurographics 1999, pp. 245–256 (1999)
Boughorbel, F., Page, D., Dumont, C., Abidi, M.A.: Registration and integration of multi-sensor data for photo-realistic scene reconstruction. In: Proc. Applied Imagery Pattern Recognition, pp. 74–84 (1999)
Umeda, K., Godin, G., Rioux, M.: Registration of range and color images using gradient constrains and range intensity images. In: Proc. of 17th Int. Conf. on Pattern Recognition, vol. 3, pp. 12–15 (2004)
Kurazume, R., Nishino, K., Zhang, Z., Ikeuchi, K.: Simultaneous 2D images and 3D geometric model registration for texture mapping utilizing reflectance attribute. In: Proc. Fifth ACCV, pp. 99–106 (2002)
Elstrom, M.D., Smith, P.W.: Stereo-based registration of multi-sensor imagery for enhanced visualization of remote environments. In: Proc. of the 1999 Int. Conf. on Robotics Automation, pp. 1948–1953 (1999)
Boehm, J., Becker, S.: Automatic Marker-Free Registration of Terrestrial Laser Scans using Reflectance Features. In: 8th Conf. on Optical 3D Measurement Techniques (2007)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)
Fischler, M.A., Bolles, R.C.: Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM 16(24), 381–395 (1981)
Shinozaki, M., Kusanagi, M., Umeda, K., Godin, G., Rioux, M.: Correction of color information of a 3D model using a range intensity image. Comput. Vis. and Image Understanding 113(11), 1170–1179 (2009)
ShapeGrabber, http://www.shapegrabber.com
Zhang, Z.: A flexible new techniques for camera calibration. IEEE Trans. Pattern Anal. Martch. Intell. 22(11), 1330–1334 (2000)
PolyWorks, http://www.innovmetric.com
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Inomata, R., Terabayashi, K., Umeda, K., Godin, G. (2011). Registration of 3D Geometric Model and Color Images Using SIFT and Range Intensity Images. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2011. Lecture Notes in Computer Science, vol 6938. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24028-7_30
Download citation
DOI: https://doi.org/10.1007/978-3-642-24028-7_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24027-0
Online ISBN: 978-3-642-24028-7
eBook Packages: Computer ScienceComputer Science (R0)