Abstract
We describe a graph cut algorithm to recover the 3D object surface using both silhouette and foreground color information. The graph cut algorithm is used for optimization on a color consistency field. Constraints are added to improve its performance. These constraints are a set of predetermined locations that the true surface of the object is likely to pass through. They are used to preserve protrusions and to pursue concavities respectively in the first and the second phase of the algorithm. We also introduce a method for dealing with silhouette uncertainties arising from background subtraction on real data. We test the approach on synthetic data with different numbers of views (8, 16, 32, 64) and on a real image set containing 30 views of a toy squirrel.
This work is supported by the NSF grant IIS-0325715 entitled ITR: New Technology for the Capture, Analysis and Visualization of Human Movement.
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References
Szeliski, R.: Rapid octree construction from image sequences. CVGIP: Image Understanding 57, 23–32 (1993)
Cheung, G.K.M., Baker, S., Kanade, T.: Visual hull alignment and refinement across time: A 3d reconstruction algorithm combining shape-from-silhouette with stereo. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR 2003), pp. 375–382 (2003)
Snow, D., Viola, P., Zabih, R.: Exact voxel occupancy with graph cuts. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR 2000), pp. 345–352 (2000)
Paris, S., Sillion, F., Long, L.: A surface reconstruction method using global graph cut optimization. In: Proc. Asian Conf. Computer Vision, ACCV 2004 (2004)
Kutulakos, K., Seitz, S.: A theory of shape by space carving. In: Proc. IEEE Int’l Conf. Computer Vision (ICCV-1999), pp. 307–314 (1999)
Solem, J., Kahl, F., Heyden, A.: Visibility constrained surface evolution. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR-2005), pp. 892–900 (2005)
Isidoro, J., Sclaroff, S.: Stochastic refinement of the visual hull to satisfy photometric and silhouette consistency constraints. In: Proc. IEEE Int’l Conf. Computer Vision (ICCV-2003), pp. 1335–1342 (2003)
Vogiatzis, G., Torr, P., Cippola, R.: Multi-view stereo via volumetric graph-cuts. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR-2005), pp. 391–399 (2005)
Sinha, S.N., Pollefeys, M.: Multi-view reconstruction using photo-consistency and exact silhouette constraints: A maximum-flow formulation. In: Proc. IEEE Int’l Conf. Computer Vision (ICCV-2005), pp. I:349–356 (2005)
Boykov, Y., Kolmogorov, V.: Computing geodesics and minimal surfaces via graph cuts. In: Proc. IEEE Int’l Conf. Computer Vision (ICCV-2003), pp. 26–33 (2003)
Boykov, Y., Veksler, O., Zabih, R.: Fast approximate energy minimization via graph cuts. IEEE Trans. Pattern Anal. and Machine Intell. 23, 1222–1239 (2001)
Boykov, Y., Jolly, M.P.: Interactive graph cuts for optimal boundary and region segmentation of objects in n-d images. In: Proc. IEEE Int’l Conf. Computer Vision (ICCV-2001), pp. 105–112 (2001)
Esteban, C.H., Schmitt, F.: Silhouette and stereo fusion for 3d object modeling. In: Proc. 4th Int’l Conf. on 3D Digital Imaging and Modeling (3DIM 2003), pp. 46–53 (2003)
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© 2006 Springer-Verlag Berlin Heidelberg
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Tran, S., Davis, L. (2006). 3D Surface Reconstruction Using Graph Cuts with Surface Constraints. In: Leonardis, A., Bischof, H., Pinz, A. (eds) Computer Vision – ECCV 2006. ECCV 2006. Lecture Notes in Computer Science, vol 3952. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11744047_17
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DOI: https://doi.org/10.1007/11744047_17
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