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access icon free Simple method for generating evaluation data for scene flow algorithms

A method which simply generates evaluation data for scene flow testing is presented, in which most algorithms for scene flow estimation require a pair of depth and colour images. We scanned the 3D geometry of a real object and rendered it in graphical space to acquire its depth and colour information. Next, surface deformation to generate natural motion for the scene flow is employed. The surface deformation enabled the generation of non-rigid as well as rigid motion. Next, barycentric coefficients were exploited to compute the ground truth scene flow. The coefficients enabled the acquired dense scene to flow accurately without dense point tracking and colour information. On the other hand, each flow vector was computed per pixel and a pixel could come from more than one point due to occlusion. We compute the flow maps dividing the geometry into the front area and the occluded area by modifying a z-buffer algorithm. Experimental results showed that the proposed method reliably provided evaluation data (a pair of depth and colour images and a scene flow map) based on a scanned model with occlusion.

References

    1. 1)
      • 7. Fix, G.J.: ‘A rational finite element basis’, Eugene L. Wachspress, Soc. Ind. Appl. Math. Rev., 1978, 20, (1), pp. 195196.
    2. 2)
      • 3. Huguet, F., Devernay, F.: ‘A variational method for scene flow estimation from stereo sequences’. Int. Conf. Computer Vision, Rio de Janeiro, Brazil, October 2007, pp. 1421, doi 10.1109/ICCV.2007.44090003.
    3. 3)
      • 6. Sumner, R.W., Schmid, J., Pauly, M.: ‘Embedded deformation for shape manipulation’. ACM SIGGRAPH, San Diego, CA, USA, August 2007, pp. 80:180:7, doi: 10.1145/1275808.12764782007.
    4. 4)
      • 8. Rensselaer Polytechnic Institute: ‘Point inclusion in polygon test W. Randolph franklin (WRF)’. Available at https://wrf.ecse.rpi.edu//Research/Short_Notes/pnpoly.html, accessed October 2017.
    5. 5)
      • 2. Geiger, A., Lenz, P., Urtasun, R.: ‘Are we ready for autonomous driving? The KITTI vision benchmark suite’. IEEE Conf. Computer Vision and Pattern Recognition, Washington, DC, USA, June 2012, pp. 33543361.
    6. 6)
    7. 7)
      • 4. Butler, D.J., Wulff, J., Stanley, G.B., et al: ‘A naturalistic open source movie for optical flow evaluation’. European Conf. Computer Vision, Firenze, Italy, October 2012, pp. 611625, doi: 0.1007/978-3-642-33783-3_44.
    8. 8)
      • 5. Sun, D., Sudderth, E.B., Pfister, H.: ‘Layered RGBD scene flow estimation’. Proc. Int. Conf. Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA, June 2015.
http://iet.metastore.ingenta.com/content/journals/10.1049/el.2018.6856
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