Pallavi Raiturkar
Papers
2
Total Citations
21
H-Index
2
About
Pallavi Raiturkar is a researcher whose work sits at the intersection of human-computer interaction, visual attention modeling, and virtual reality (VR). Her primary research focus is on understanding and quantifying how users perceive and attend to visual information in immersive 360° environments. She is best known for her pioneering benchmarking work on saliency map generation from eye-tracking data in VR. Her most-cited paper, "A Benchmark of Four Methods for Generating 360° Saliency Maps from Eye Tracking Data" (2019, 16 citations), systematically evaluates different computational approaches for modeling user gaze patterns in spherical video. This work is foundational for applications ranging from gaze prediction and robotics to video compression and rendering optimization. By rigorously comparing methods, she provided the VR community with critical insights into which techniques best capture human visual attention in omnidirectional scenes. Her contributions help bridge the gap between raw eye-tracking data and practical saliency models, enabling more efficient and perceptually-aware VR systems. Raiturkar’s research is essential reading for anyone working on attention-aware rendering, immersive media compression, or human-centered VR design.
Research Focus
Key Achievements
Top Papers
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