Abstract
We present a tool for the acquisition of 3D textured models of objects of desktop size using an hybrid computer vision framework. This framework combines active laser-based triangulation with passive motion estimation. The 3D models are obtained by motion-based alignment (with respect to a fixed world frame) of imaged laser profiles backprojected onto time-varying camera frames. Two distinct techniques for estimating camera displacements are described and evaluated. The first is based on a Simultaneous Localization and Mapping (SLAM) approach, while the second exploits a planar pattern in the scene and recovers motion by homography decomposition. Results obtained with a custom laser-camera stereo setup — implemented with off-the-shelf hardware — show that a trade-off exists between the greater operational flexibility of SLAM and the higher model accuracy of the homography-based approach.
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References
Chen, F., Brown, G.M., Song, M.: Overview of Three-Dimensional Shape Measurement using Optical Methods. Optical Engineering 39, 10–22 (2000)
Bernardini, F., Rushmeier, H.E.: The 3D Model Acquisition Pipeline. Computer Graphics Forum 21, 149–172 (2002)
Rocchini, C., Cignoni, P., Montani, C., Scopigno, R.: A low cost 3D scanner based on structured light. Computer Graphics Forum 20, 299–308 (2001)
Winkelbach, S., Molkenstruck, S., Wahl, F.M.: Low-cost laser range scanner and fast surface registration approach. In: Franke, K., Müller, K.-R., Nickolay, B., Schäfer, R. (eds.) DAGM 2006. LNCS, vol. 4174, pp. 718–728. Springer, Heidelberg (2006)
Habbecke, M., Kobbelt, L.: Laser brush: a flexible device for 3D reconstruction of indoor scenes. In: Proceedings of the 2008 ACM Symposium on Solid and Physical Modeling, pp. 231–239. ACM, New York (2008)
Colombo, C., Comanducci, D., Del Bimbo, A.: Shape reconstruction and texture sampling by active rectification and virtual view synthesis. Computer Vision and Image Understanding 115, 161–176 (2011)
Agarwal, S., Snavely, N., Simon, I., Seitz, S.M., Szeliski, R.: Building Rome in a Day. In: Proceedings of the International Conference on Computer Vision, ICCV 2009, Kyoto, Japan (2009)
Farenzena, A.M., Fusiello, A., Gherardi, R.: Structure-and-Motion Pipeline on a Hierarchical Cluster Tree. In: Proceedings of the IEEE International Workshop on 3-D Digital Imaging and Modeling, Kyoto, Japan (2009)
Vogiatzis, G., Hernàndez, C.: Video-based, real-time multi view stereo. Image and Vision Computing (2011)
Wang, L., Liao, M., Gong, M., Yang, R., Nistèr, D.: High-quality real-time stereo using adaptive cost aggregation and dynamic programming. In: 3rd Int. Symposium on 3D Data Processing, Visualization and Transmission (3DPVT), pp. 798–805. Springer (2006)
Zhang, R., Tsai, P.S., Cryer, J.E., Shah, M.: Shape from shading: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 21(8), 690–706 (1999)
Bouguet, J.Y., Perona, P.: 3D Photography Using Shadows in Dual-Space Geometry. International Journal of Computer Vision (IJCV) 35, 129–149 (1999)
Hernàndez, C., Vogiatzis, G., Cipolla, R.: Multi-view Photometric Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence 30 (2008)
Zhang, Z.: A flexible new technique for camera calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 1330–1334 (2000)
Bradski, G.R.: Computer Vision Face Tracking for Use in a Perceptual User Interface. Intel Technology Journal (1998)
Fisher, R.B., Naidu, D.K.: A Comparison of Algorithms for Subpixel Peak Detection. In: Image Technology, Advances in Image Processing, Multimedia and Machine Vision, pp. 385–404. Springer (1996)
Klein, G., Murray, D.: Parallel tracking and mapping for small AR workspaces. In: Proc. Sixth IEEE and ACM International Symposium on Mixed and Augmented Reality (ISMAR 2007), Nara, Japan (November 2007)
Mei, C., Sibley, G., Cummins, M., Newman, P., Reid, I.: RSLAM: A system for large-scale mapping in constant-time using stereo. International Journal of Computer Vision, 1–17 (2010); Special issue of BMVC
Shi, J., Tomasi, C.: Good features to track. Technical report, Ithaca, NY, USA (1993)
Bouguet, J.Y.: Pyramidal implementation of the Lucas-Kanade feature tracker description of the algorithm (2000)
Hartley, R.I., Zisserman, A.: Multiple View Geometry in Computer Vision, 2nd edn. Cambridge University Press (2004) ISBN: 0521540518
Lourakis, M.I.A., Argyros, A.A.: SBA: a software package for generic sparse bundle adjustment. ACM Transactions on Mathematical Software, 1–30 (2009)
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Fanfani, M., Colombo, C. (2013). LaserGun: A Tool for Hybrid 3D Reconstruction. In: Chen, M., Leibe, B., Neumann, B. (eds) Computer Vision Systems. ICVS 2013. Lecture Notes in Computer Science, vol 7963. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39402-7_28
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DOI: https://doi.org/10.1007/978-3-642-39402-7_28
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