Stereoscopic static depth perception of enclosed 3D objects

ABSTRACT

Depth perception of semi-transparent virtual objects and the visualization of their spatial layout are crucial in many applications, in particular medical applications. Depth cues for opaque objects have been extensively studied, but this is not the case for stereoscopic semi-transparent objects, in particular in the case when one 3D object is enclosed within a larger exterior object.

In this work we explored different stereoscopic rendering methods to analyze their impact on depth perception accuracy of an enclosed 3D object. Two experiments were performed: the first tested the hypotheses that depth perception is dependent on the color blending of objects (opacity - alpha) for each rendering method and that one of two rendering methods used is superior. The second experiment was performed to corroborate the results of the first experiment and to test an extra hypothesis: is depth perception improved if an auxiliary object that provides a relationship between the enclosed object and the exterior is used?

The first rendering method used is simple alpha blending with Blinn-Phong shading model, where a segmented brain (exterior object) and a synthetic tumor (enclosed object) were blended. The second rendering method also uses Blinn-Phong, but the shading was modified to preserve silhouettes and to provide an illustrative rendering. Comparing both rendering methods, the brighter regions of the first rendering method will become more transparent in the second rendering method, thus preserving silhouette areas.

The results show that depth perception accuracy of an enclosed object rendered with a stereoscopic system is dependent on opacity for some rendering methods (simple alpha blending), but this effect is less pronounced than the dependence on object position in relation to the exterior object. The illustrative rendering method is less dependent on opacity. The different rendering methods also perform slightly differently; an illustrative rendering method was superior and the use of an auxiliary object seems to facilitate depth perception.

References

1. Akerstrom, R. A., and Todd, J. T. 1988. The perception of stereoscopic transparency. Perception & Psychophysics 44, 5, 421--432.
2. Bianchi, G. 2006. Exploration of Augmented Reality Technology for Surgical Training Simulators. PhD thesis, Swiss Federal Institute of Technology Zurich.
3. Bimber, O., Encarnação, L. M., and Schmalstieg, D. 2000. Augmented reality with back-projection systems using transflective surfaces. Computer Graphics Forum (Proceedings of EUROGRAPHICS 2000) 19, 3, 161--168.
4. Bimber, O., Fröhlich, B., Schmalstieg, D., and Encarnação, L. M. 2001. The virtual showcase. IEEE Computer Graphics and Applications 21, 6, 48--55.
5. Blinn, J. F. 1977. Models of light reflection for computer synthesized pictures. ACM SIGGRAPH Computer Graphics 11, 2, 192--198.
6. Boháč, M. Dual monitor set--up for stereoscopic viewing. http://klub.stereofotograf.eu/dual_monitor.php.
7. Boucheny, C., Bonneau, G., Droulez, J., Thibault, G., and Ploix, S. 2009. A perceptive evaluation of volume rendering techniques. ACM Transactions on Applied Perception (TAP) 5, 4 (January).
8. Carnecky, R., Fuchs, R., Mehl, S., Jang, Y., and Peikert, R. 2013. Smart transparency for illustrative visualization of complex flow surfaces. IEEE Transactions on Visualization and Computer Graphics 19, 5 (May), 838--851.
9. Chan, M., Wu, Y., Mak, W., Chen, W., and Qu, H. 2009. Perception-based transparency optimization for direct volume rendering. IEEE Transactions on Visualization and Computer Graphics 15, 6 (November/December), 1283--1290.
10. Cutting, J. E., and Vishton, P. M., 1995. Perceiving layout and knowing distances: The integration, relative potency, and contextual use of different information about depth. In W. Epstein and S. Rogers (Eds.), Perception of space and motion, New York: Accademic Press.
11. Hart, J. Dual monitor digital viewing. http://www.crystalcanyons.net/abouts/3dc9_DualMonitorComplete.shtm.
12. Interrante, V., Fuchs, H., and Pizer, S. M. 1997. Conveying the 3d shape of smoothly curving transparent surfaces via texture. IEEE Transactions on Visualization and Computer Graphics 3, 2 (April/June), 98--117.
13. Johnson, S. P., and Aslin, R. N. 2000. Infants' perception of transparency. Developmental Psychology 36, 6, 808--816.
14. Kersten, M. A., Stewart, A. J., Troje, N., and Ellis, R. 2006. Enhancing depth perception in translucent volumes. IEEE Transactions on Visualization and Computer Graphics 12, 5 (September/October), 1117--1123.
15. Maurer, C. R., Jr., Sauer, F., Hu, B., Bascle, B., Geiger, B., Wenzel, F., Rohlfing, T., Brown, C. M., Bakos, R. S., Maciunas, R. J., and Bani-hashemi, A. 2001. Augmented reality visualization of brain structures with stereo and kinetic depth cues: System description and initial evaluation with head phantom. In Medical Imaging 2001: Visualization, Display, and Image-Guided Procedures, Proc. SPIE 4319, Society of Photo-Optical Instrumentation Engineers, 445--456.
16. Nakayama, K., Shimojo, S., and Ramachandran, V. S. 1990. Transparency: relation to depth, subjective contours, luminance, and neon color spreading. Perception 19, 497--513.
17. Salah, Z., Bartz, D., and Strasser, W. 2005. Illustrative rendering of segmented anatomical data. In In Proc. of Simulation und Visualisierung, 175--184.
18. Schwartz, J. T., 2003. Test yourself for stereo blindness. http://www.settheory.com/stereo_blindness_test.html.
19. Sielhorst, T., Bichlmeier, C., Heining, S. M., and Navab, N. 2006. Depth perception - a major issue in medical ar: Evaluation study by twenty surgeons. In Proceedings of MICCAI 2006, LNCS 4190, 364--372.
20. Sielhorst, T., Feuerstein, M., and Navab, N. 2008. Advanced medical displays: A literature review of augmented reality. Journal of Display Technology 4, 4, 451--467.
21. Todd, J. T. 2004. The visual perception of 3d shape. TRENDS in Cognitive Sciences 8, 3, 115--121.
22. Tsirlin, I., Allison, R. S., and Wilcox, L. M. 2008. Stereoscopic transparency: Constraints on the perception of multiple surfaces. Journal of Vision 8, 5 (May), 1--10.
23. Wang, C., Frimmel, H., and Smedby, Ö. 2011. Level-set based vessel segmentation accelerated with periodic monotonic speed function. In Medical Imaging 2011: Image Processing, Proc. SPIE 7962, Society of Photo-Optical Instrumentation Engineers.
24. Zheng, L., Wu, Y., and Ma, K. 2013. Perceptually Based Depth-Ordering Enhancement for Direct Volume Rendering. IEEE Transactions on Visualization and Computer Graphics 19, 3, 446--459.

Index Terms

• Computing methodologies

  • Artificial intelligence

    • Computer vision

      • Computer vision problems

        • Object recognition

      • Computer vision tasks

        • Scene understanding

  • Computer graphics

    • Graphics systems and interfaces

      • Virtual reality