Vivek Venugopalan

About

Vivek Venugopalan is a leading researcher in computer vision and robotics, with a primary focus on deep learning for scene understanding and perception. His most influential work centers on automated occlusion edge detection in RGB-D frames—a critical prerequisite for tasks like object manipulation, navigation, and 3D reconstruction. His 2016 paper, "Deep Learning for Automated Occlusion Edge Detection in RGB-D Frames," has garnered 26 citations, demonstrating its impact on the field. Earlier, his 2014 work on the same topic laid the foundation for using deep convolutional networks to extract occlusion edges from images and videos, addressing the challenge of identifying range discontinuities without relying solely on depth sensors. Venugopalan’s contributions are particularly notable for enabling mobile robots and vision systems to better understand complex environments by detecting boundaries where objects overlap or occlude one another. His research bridges the gap between raw sensor data and actionable spatial information, making him a key figure in advancing autonomous systems and visual perception.

Research Focus

Key Achievements

Top Papers

1. 1
   Deep Learning for Automated Occlusion Edge Detection in RGB-D Frames

   26 citations · 2016
2. 2
   Occlusion Edge Detection in RGB-D Frames using Deep Convolutional Networks

   9 citations · 2014

Key Collaborators

Contact & Links