Deepti Hegde

Papers

Total Citations

H-Index

About

Deepti Hegde is a researcher whose work lies at the intersection of computer vision, 3D mapping, and resource-constrained computing. Her primary contributions focus on enabling robust camera relocalization—the ability to determine a camera’s position within a pre-built 3D map—on devices with limited memory, such as smartphones or embedded systems. Her most-cited paper, “Relocalization of Camera in a 3D Map on Memory Restricted Devices” (2020, 5 citations), addresses a critical challenge in augmented reality and robotics: maintaining accurate spatial awareness without the computational overhead of large-scale 3D models. This work is notable for its practical approach to balancing accuracy and efficiency, making it relevant for real-world applications where hardware constraints are unavoidable. While her citation count is modest, Hegde’s research fills an important niche in the broader field of visual localization, offering solutions that bridge the gap between theoretical mapping techniques and deployable systems. Her contributions are particularly valuable for students and engineers working on mobile AR, autonomous navigation, or IoT-based vision systems, where memory and processing power are at a premium.