Purvesh Sharma
Papers
2
Total Citations
15
H-Index
2
About
Purvesh Sharma is a researcher at the forefront of computer vision and robotic automation, specializing in 3D object detection and deep convolutional neural network (CNN) design. His work addresses a critical transition in industrial automation: moving from traditional 2D RGB image processing to advanced 3D depth-sensing technologies. Sharma’s major contributions include pioneering CNN architectures tailored for RGB-D (Red-Green-Blue plus Depth) images, which significantly enhance the accuracy and reliability of object detection for robotic grasping. His most-cited paper, "Deep Convolutional Neural Network Design Approach for 3D Object Detection for Robotic Grasping" (2020, 10 citations), introduces a novel framework that leverages economical 3D sensors to improve automation in manufacturing and logistics. A second influential work, "Backbone Neural Network Design of Single Shot Detector from RGB-D Images for Object Detection" (2020, 5 citations), further refines real-time detection capabilities. While his citation counts reflect a growing field, Sharma’s research is notable for its practical impact on bridging the gap between machine learning theory and real-world robotic applications. His work is essential reading for students and engineers aiming to advance intelligent automation in dynamic environments.
Research Focus
Key Achievements
Top Papers
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- 2