"Blur in images can create the sensation of depth because it emulates an optical property of the eye; namely, the limited depth of field created by the eye's lens. When the human eye looks at an object, this object appears sharp on the retina, but objects at different distances appear blurred. Advances in gaze-tracking technologies enable us to reproduce dynamic depth of field in regular displays, providing an alternative way of conveying depth. In this project we investigate gaze-contingent depth of field as a method to produce realistic 3D images, and analyze how effectively people can use it to perceive depth. We found that GC DOF increases subjective perceived realism and depth and can contribute to the perception of ordinal depth and distance between objects, but it is limited in its accuracy."

My Contribution to the Project

• design and implementation of the algorithm to render variable DOF in response to real time eye-tracker data
• design, implementation, execution and evaluation of the user study testing whether the gaze-contingent DOF affects peoples depth perception
• co-writing the research paper
• presenting the research results at the 2014 CHI Conference on Human Factors in Computing Systems

Team

• Michael Mauderer
• Simone Conte
• Miguel A. Nacenta
• Dhanraj Vishwanath

Publications

• Depth Perception with Gaze-contingent Depth of Field Honorable Mention Award Michael Mauderer, Simone Conte, Miguel A. Nacenta and Dhanraj Vishwanath. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, ACM, 2014 (PDF)

Tech

• Python
  • PsychoPy
  • Pylink
  • Matplotlib
  • Colour
• Blender
• Eye-Link 1000
• SPSS
• LaTeX