We present a novel pipeline for localizing a free roaming eye tracker within a LiDAR-based 3D reconstructed scene with high levels of accuracy. By utilizing a combination of reconstruction algorithms that leverage the strengths of global versus local capture methods and user-assisted refinement, we reduce drift errors associated with Dense Simultaneous Localization and Mapping (D-SLAM) techniques. Our framework supports region-of-interest (ROI) annotation and gaze statistics generation and the ability to visualize gaze in 3D from an immersive first person or third person perspective. This approach gives unique insights into viewers' problem solving and search task strategies and has high applicability in indoor static environments such as crime scenes.
Library of Congress Subject Headings
Eye tracking--Data processing; Optical radar; Three-dimensional imaging
Computer Science (MS)
Department, Program, or Center
Computer Science (GCCIS)
Pieszala, James, "3D Gaze Point Localization and Visualization Using LiDAR-based 3D Reconstructions" (2015). Thesis. Rochester Institute of Technology. Accessed from
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