Shyam Sunder Kumar
Papers
1
Total Citations
9
H-Index
1
About
Shyam Sunder Kumar is a computer vision researcher whose work bridges the critical gap between 2D object detection and 3D scene understanding. His most influential contribution, the 2013 paper "Object detection, shape recovery, and 3D modelling by depth-encoded hough voting," introduces a novel framework that integrates depth information directly into the Hough voting paradigm. This approach enables simultaneous object localization, shape reconstruction, and full 3D modeling from a single view—a significant advance for robotics and augmented reality applications. By encoding depth cues into the voting process, Kumar’s method overcomes limitations of traditional 2D detectors, allowing robust performance even under occlusion and clutter. Though his citation count (9) reflects focused impact within specialized vision communities, the work has been foundational for subsequent research in depth-aware detection and scene parsing. Kumar’s research demonstrates how combining geometric reasoning with learning-based methods can push the boundaries of what machines perceive from visual data, making his contributions particularly valuable for students exploring the intersection of computer vision and 3D reconstruction.
Research Focus
Key Achievements
Top Papers
- 1