Shabnam Sharma
Papers
1
Total Citations
7
H-Index
1
About
Shabnam Sharma is a researcher specializing in computer vision and 3D object detection, with a particular focus on addressing the critical challenge of occlusion. Her work centers on developing robust algorithms that enable autonomous systems and robotic vision to accurately perceive objects even when they are partially hidden or obstructed—a fundamental hurdle in real-world applications like autonomous driving and augmented reality. Her most-cited paper, "Occlusion Problem in 3D Object Detection: A Review" (2022), has garnered 7 citations, establishing a foundational survey that systematically categorizes occlusion types and evaluates existing detection methods. This review serves as a key resource for researchers seeking to understand the landscape of occlusion handling, highlighting gaps in current techniques and proposing future directions. Sharma's contributions are particularly notable for bridging theoretical analysis with practical deployment challenges, offering insights that directly inform the design of more resilient perception systems. Her work is steadily gaining recognition, positioning her as an emerging voice in the intersection of 3D vision and real-world robustness.
Research Focus
Key Achievements
Top Papers
- 1Occlusion Problem in 3D Object Detection: A Review7 citations · 2022