3D Research

, 9:29 | Cite as

Perceptually Improved 3D Object Representation Based on Guided Adaptive Weighting of Feature Channels of a Visual-Attention Model

  • Ghazal Rouhafzay
  • Ana-Maria Cretu
3DR Express


Real-time interaction in virtual environments composed of numerous objects modeled with a high number of faces remains an important issue in interactive virtual environment applications. A well-established approach to deal with this problem is to simplify small or distant objects where minor details are not informative for users. Several approaches exist in literature to simplify a 3D mesh uniformly. A possible improvement to this approach is to take advantage of a visual attention model to distinguish regions of a model which are considered important from the point of view of the human visual system. These regions can then be preserved during simplification to improve the perceived quality of the model. In the present article, we present an original application of biologically-inspired visual attention for improved perception-based representation of 3D objects. An enhanced visual attention model extracting information about color, intensity, orientation, as in the classical bottom-up visual attention model, but that also considers supplementary features believed to guide the deployment of human visual attention (such as symmetry, curvature, contrast, entropy and edge information), is introduced to identify such salient regions. Unlike the classical model where these features contribute equally to the identification of salient regions, a novel solution is proposed to adjust their contribution to the visual-attention model based on their compliance with points identified as salient by human subjects. An iterative approach is then proposed to extract salient points from salient regions. Salient points derived from images taken from best viewpoints of a 3D object are then projected to the surface of the object to identify salient vertices which will be preserved in the mesh simplification. The obtained results are compared with existing solutions from the literature to demonstrate the superiority of the proposed approach.


Interest point and salient region detections Visual attention Visual perception 3D mesh Simplification Level-of-detail 



This work is supported in part by the Natural Sciences and Engineering Research Council of Canada (NSERC).

Compliance with Ethical Standards

Conflict of interest

The authors declare that they have no conflict of interest.


  1. 1.
    Kietzmann, T. C., Lange, S., & Riedmiller, M. (2009). Computational object recognition: A biologically motivated approach. Biological Cybernetics, 100, 59–79.MathSciNetCrossRefGoogle Scholar
  2. 2.
    Luebke, D., & Hallen, B. (2001). Perceptually driven simplification for interactive rendering. In S. J. Gortler & K. Myszkowski (Eds.), Rendering techniques. Eurographics. Vienna: Springer.Google Scholar
  3. 3.
    Itti, L., Koch, C., & Niebur, E. (1998). A model of saliency-sased visual attention for rapid scene analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(11), 1254–1259.CrossRefGoogle Scholar
  4. 4.
    Hadizadeh, H., & Bajic, I. V. (2014). Saliency-aware video compression. IEEE Transactions on Image Processing, 23(1), 19–33.MathSciNetCrossRefGoogle Scholar
  5. 5.
    Frintrop, S., Rome, E., & Christensen, H. I. (2010). Computational visual attention systems and their cognitive foundations: A survey. ACM Transactions on Applied Perception (TAP), 7(1), 6.Google Scholar
  6. 6.
    Chagnon-Forget, M., Rouhafzay, G., Cretu, A.-M., & Bouchard, S. (2016). Enhanced visual-attention model for perceptually-improved 3d object modeling in virtual environments. 3D Research, 7(4), 1–18.CrossRefGoogle Scholar
  7. 7.
    Rouhafzay, G., & Cretu, A. -M. (2017). Selectively-densified mesh construction for virtual environments using salient points derived from a computational model of visual attention. In 2017 IEEE international conference on computational intelligence and virtual environments for measurement systems and applications (CIVEMSA), Annecy, 2017 (pp. 99–104).Google Scholar
  8. 8.
    Luebke, D., Reddy, M., Cohen, J. D., Varshney, A., Watson, B., & Huebner, R. (2003). Level of details for 3D graphics. Amsterdam: Morgan Kaufmann.Google Scholar
  9. 9.
    Pojar, E., & Schmalstieg, D. (2003). User-controlled creation of multiresolution meshes. In Proceedings of the symposium on Interactive 3D graphics (pp. 127–130). Monterey, CA.Google Scholar
  10. 10.
    Kho, Y., & Garland, M. (2003). User-guided simplification. In Proceedings of ACM symposium on interactive 3D graphics (pp. 123–126).Google Scholar
  11. 11.
    Ho, T. -C., Lin, Y. -C., Chuang, J. -H., Peng, C. -H. & Cheng, Y. -J. (2006). User-assisted mesh simplification. In Proceedings of ACM international conference on virtual-reality continuum and its applications (pp. 59–66).Google Scholar
  12. 12.
    Lee, C. H., Varshney, A., & Jacobs, D. W. (2005). Mesh saliency. ACM SIGGRAPH, 174, 659–666.CrossRefGoogle Scholar
  13. 13.
    Borji, A., & Itti, L. (2013). State-of-the-art in visual attention modeling. IEEE Transaction on Pattern Analysis and Machine Intelligence, 35(1), 185–207.CrossRefGoogle Scholar
  14. 14.
    Frintrop, S. (2006). The visual attention system VOCUS: Top-down extension. In J. G. Carbonell & J. Siekmann (Eds.), VOCUS: A visual attention system for object detection and goal-directed search. Lecture notes in computer science (Vol. 3899, pp. 55–86). Berlin: Springer.CrossRefGoogle Scholar
  15. 15.
    Castellani, U., Cristani, M., Fantoni, S., & Murino, V. (2008). Sparse points matching by combining 3D mesh saliency. Eurographics, 27, 643–652.Google Scholar
  16. 16.
    Zhao, Y., Liu, Y., Wang, Y., Wei, B., Yang, J., Zhao, Y., et al. (2016). Region-based saliency estimation for 3D shape analysis and understanding. Neurocomputing, 197(2016), 1–13.CrossRefGoogle Scholar
  17. 17.
    Lavoué, G., Cordier, F., Seo, H., & Larabi, M.-C. (2018). Visual attention for rendered 3D shapes. Computer Graphics Forum, 37(2), 191–203.CrossRefGoogle Scholar
  18. 18.
    Godil, A., & Wagan, A. I. (2011). Salient local 3D features for 3D shape retrieval. SPIE 3D Image Processing and Application, 7864, 78640S.Google Scholar
  19. 19.
    Sipiran, I., & Bustos, B. (2010). A robust 3D interest points detector based on Harris operator. In Eurographics 2010 Workshop on 3D Object Retrieval (3DOR’10) (pp. 7–14).Google Scholar
  20. 20.
    Novatnak, J., & Nishino, K. (2007). Scale-dependent 3D geometric features. In IEEE international conference on computer vision (pp. 1–8).Google Scholar
  21. 21.
    Sun, J., Ovsjanikov, M., & Guibas, L. (2009). A concise and provably informative multi-scale signature based on heat diffusion. In Eurographics symposium on geometry processing (Vol. 28, pp. 1383–1392).CrossRefGoogle Scholar
  22. 22.
    Mirloo, M., & Ebrahimnezhad, H. (2018). Salient point detection in protrusion parts of 3D object robust to isometric variations. 3D Research, 9, 2.CrossRefGoogle Scholar
  23. 23.
    Alliez, P., Cohen-Steiner, D., Devillers, O., Levy, B., & Desbrun, M. (2003). Anisotropic polygonal remeshing. ACM Siggraph, 22(3), 485–493.CrossRefGoogle Scholar
  24. 24.
    Song, R., Liu, Y., Zhao, Y., Martin, R. R., & Rosin, P. L. (2012). Conditional random field-based mesh saliency. In IEEE international conference on image processing (pp. 637–640).Google Scholar
  25. 25.
    Howlett, S., Hammil, J., & O’Sullivan, C. (2005). An experimental approach to predicting saliency for simplified polygonal models. ACM Transaction on Applied Perception, 2(3), 1–23.Google Scholar
  26. 26.
    Harel, J., Koch, C., & Perona, P. (2006). Graph-based visual saliency. In Proceedings of the neural information processing systems (pp. 545–552).Google Scholar
  27. 27.
    Loy, G., & Eklundh, J. -O. (2006). Detecting symmetry and symmetric constellations of features. In IEEE ECCV (pp. 508–521).CrossRefGoogle Scholar
  28. 28.
    Derrington, A. M., Krauskopf, J., & Lennie, P. (1984). Chromatic mechanisms in lateral geniculate nucleus of macaque. The Journal of Physiology, 357, 241–265.CrossRefGoogle Scholar
  29. 29.
    Wolfe, J. M., & Horowitz, T. S. (2004). What attributes guide the deployment of visual attention and how do they do it? Nature Reviews Neuroscience, 5, 1–7.CrossRefGoogle Scholar
  30. 30.
    Dutagaci, H., Cheung, C. -P., Godil, A. (2016) A benchmark for 3D interest points marked by human subjects. Accessed August 1, 2017.
  31. 31.
    Hughes, H. C., & Zimba, L. D. (1987). Natural boundaries for the spatial spread of directed visual attention. Neuropsychologia, 25(1), 5–18.CrossRefGoogle Scholar
  32. 32.
    Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004). Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing, 13(4), 600–612.CrossRefGoogle Scholar
  33. 33.
    Moller, T., & Trumbore, B. (1997). Fast, minimum storage ray/triangle intersection. Journal of Graphics Tools, 2(1), 21–28.CrossRefGoogle Scholar
  34. 34.
    Garland, M., & Heckbert, P. S. (1997). Surface simplification using quadric error meshes. In SIGGRAPH '97 proceedings of the 24th annual conference on computer graphics and interactive techniques (pp. 209–216).Google Scholar
  35. 35.
    Cignoni, P., Rocchini, C., & Scopigno, R. (1998). Metro: Measuring error on simplified surfaces. Computer Graphics Forum, 17(2), 167–174.CrossRefGoogle Scholar

Copyright information

© 3D Research Center, Kwangwoon University and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Carleton UniversityOttawaCanada

Personalised recommendations