Biography


Welcome to my academic homepage!

I am a Computer Science graduate student at the University of Maryland, College Park. My research interests lie in the intersection of 3D Vision, Computer Graphics and Deep Learning with an application domain of Augmented and Virtual Reality. I have been primarily working with Prof. Matthias Zwicker on real-time neural point cloud rendering for scientific visualization and Prof. Jia-bin Huang on photo-geometric factors in 2D generative models.

Previously, I was a researcher at Innovation Labs, Tata Consultancy Services collaborating with Dr. Lovekesh Vig and Ms. Ramya Hebbalaguppe. I completed my undergrad in Computer Science and Engineering at Indraprastha Institute of Information Technology Delhi (IIITD). At IIITD, I was fortunate to work with Prof. Saket Anand and Prof. Ojaswa Sharma on interesting problems of scene reconstruction and procedural modelling.



Interests
  • Neural Rendering
  • Animation Transfer
  • Deep Learning for AR/VR/XR
Education

  • M.S. in Computer Science, 2021-2023

    University of Maryland, College Park

  • B.Tech in Computer Science and Engineering, 2017

    Indraprastha Institute of Information Technology Delhi

Recent Updates

  • 17th July 2023: Our work “Diffusion Models Beat GANs on Image Classification” published on Arxiv. Check it out here.  

  • 21st May 2023: Graduated from UMD with masters in Computer Science!  

  • 18th Oct 2021: My co-authored work “Rethinking Common Assumptions to Mitigate Racial Bias in Face Recognition Datasets” won the best paper running up award at ICCV (HTCV ‘21)! Congrats to all the co-authors.
     

  • 1st Aug 2021: My co-authored work “Rethinking Common Assumptions to Mitigate Racial Bias in Face Recognition Datasets” accepted at ICCV (HTCV ‘21) as a oral paper. My first publication as a UMD student!
     

  • 25th May 2021: I have started my grad school at University of Maryland, College Park.
     

  • 13th Apr 2021: My co-authored work “Cross-Domain Multi-task Learning for Object Detection and Saliency Estimation” accepted at CVPR (CLVISION ‘21) as a poster.
     

For older news visit here.

Kaleidoscope
The "Bi"-Cycle Saga

Projects

Deep Neural Network Compression

Compressing deep neural network models for memory constrained devices
Deep Neural Network Compression

SmartOverlays

A non-intrusive, relevant and temporally coherent overlay placement technique
Video
SmartOverlays

Draw In Air

A hand gesture based in-air touch-less interaction technique for AR applications
Video
Draw In Air

Robust 3D Reconstruction of Indoor Scenes using Deep Learning

Developing an end to end pipeline for automatic generation of 3D models of indoor scenes.
PDF Dataset Video
Robust 3D Reconstruction of Indoor Scenes using Deep Learning

Distributed Fault Tolerant Multi-Robot Area Coverage Under Limited Communication Ranges

Analysed Multi-Agent area coverage problem for surveillance purposes.
Video
Distributed Fault Tolerant Multi-Robot Area Coverage Under Limited Communication Ranges

Modelling Vegetation with L-Systems Using an Image

Procedural modelling of vegetation using context-free grammar
PDF Code
Modelling Vegetation with L-Systems Using an Image

Sketch23D

A sketching interface for rapid designing of freeform 3D models from 2D sketches
Code Poster
Sketch23D

Virtual Campus Project

Creating a virtual walk-through of IIIT-Delhi campus
Project
Virtual Campus Project

Miscellaneous

Kaleidoscope
In case you find art and geometry fascinating, here is my collection of some snaps that held my gaze. (Subject to Copyright © Srinidhi Hegde 2022.)
Srinidhi Hegde
Jul 8, 2022 1 min read Off-road Musings
The "Bi"-Cycle Saga
Peering out of the window on a lazy Sunday afternoon I noticed something peculiar about the house across the street. A break of 3000 odd house numbers between my neighbor and my house didn’t add up to me.
Srinidhi Hegde
Jan 2, 2022 7 min read Off-road Musings
The "Bi"-Cycle Saga

Recent Publications

Quickly discover relevant content by filtering publications.
S. Mukhopadhyay∗, M. Gwilliam∗, V. Agarwal, N. Padmanabhan, A. Swaminathan, S. Hegde, T. Zhou, A. Shrivastava (2023). Diffusion Models Beat GANs on Image Classification. arxiv.

PDF

M. Gwilliam∗, S. Hegde, L Tinubu, A Hanson (2021). Rethinking Common Assumptions to Mitigate Racial Bias in Face Recognition Datasets. In HTCV, ICCV.

Preprint PDF Code

A. Khattar, S. Hegde, R. Hebbalaguppe (2021). Cross-Domain Multi-task Learning for Object Detection and Saliency Estimation. In CLVISION, CVPR.

PDF

S. Yalburgi, T. Dash, R. Hebbalaguppe, S. Hegde, A. Srinivasan (2020). An Empirical Study of Iterative Knowledge Distillation for Neural Network Compression. In ESANN.

PDF

S. Hegde, R. Prasad, R. Hebbalaguppe, V. Kumar (2020). Variational Student: Learning Compact and Sparser Networks in Knowledge Distillation Framework. In ICASSP (Oral).

Preprint PDF

See all publications

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