Vedang Bhupesh Shenvi Nadkarni

Vedang Bhupesh Shenvi Nadkarni

Doing research that creates an impact in the industry and the community.

Publications

Last updated on May 16, 2025

You can also find my articles on my Google Scholar profile.

An Efficient Federated Transfer Learning Approach for Multi-UAV Systems

Published in NCC, 2025

We propose a novel multi-UAV-based federated transfer learning system which drastically reduces the computational burden, centralizes it, reduces bandwidth requirements, and makes it more secure.

Recommended citation: Vedang Bhupesh Shenvi Nadkarni, Sandeep Joshi and L. Rajya Lakshmi, "An Efficient Federated Transfer Learning Approach for Multi-UAV Systems," 2025 National Conference on Communications (NCC), New Delhi, India, 2025, pp. 1-6, doi: 10.1109/NCC63735.2025.10983600.

Download Paper | Copy BibTeX

GLiDR: Topologically Regularized Graph Generative Network for Sparse LiDAR Point Clouds

Published in CVPR, 2024

We propose GliDR, a graph generative network regularized by 0-dimensional Persistent Homolgy to densify globally consistent static LiDAR pointclouds.

Recommended citation: Prashant Kumar, Kshitij Madhav Bhat, Vedang Bhupesh Shenvi Nadkarni and Prem Kalra, "GLiDR: Topologically Regularized Graph Generative Network for Sparse LiDAR Point Clouds" in 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA, 2024, pp. 15152-15161, doi: 10.1109/CVPR52733.2024.01435

Download Paper | ArXiv preprint | Download Slides | Copy BibTeX

Differentiable SLAM Helps Deep Learning-based LiDAR Perception Tasks

Published in BMVC, 2023

We design a SLAM Loss to train LiDAR based models.

Recommended citation: Prashant Kumar, Dheeraj Vattikonda, Vedang Bhupesh Shenvi Nadkarni, Erqun Dong, and Sabyasachi Sahoo, “Differentiable SLAM helps deep learning-based lidar perception tasks” in 34th British Machine Vision Conference (BMVC), Aberdeen, UK, BMVA Press, 2023, p. 822

Download Paper | ArXiv preprint | Download Slides | Copy BibTeX