NARVis: Neural Accelerated Rendering for Real-Time Scientific Point Cloud Visualization
A high-quality neural renderer for visualizing a variety of large scientific point clouds with low motion-to-photon latency.
I am working with Prof. Sotirios Nousias at the intersection of 3D vision, computational imaging, and computer graphics. Previously, I worked on efficient neural rendering solutions for point cloud visualization with Prof. Matthias Zwicker at the University of Maryland College Park. I was also a researcher at TCS Research where I explored deep learning solutions for AR with Dr. Lovekesh Vig and Ms. Ramya Hebbalaguppe.
CV (as of Sep-2025).
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A high-quality neural renderer for visualizing a variety of large scientific point clouds with low motion-to-photon latency.
Augmenting the knowledge distillation framework with sparsity inducing ability of variational inference to learn compact and sparser networks.
A situated visualization method for overlay placement in AR and video applications respecting proximity to the object of interest, non-occlusion, and aesthetics.
Proposes a method for real-time gesture interaction for AR with egocentric view.