M. Silman
Papers
1
Total Citations
2
H-Index
1
About
M. Silman is a robotics researcher whose work focuses on advancing 3D perception and object detection in cluttered, real-world environments. Their key research area involves developing algorithms that enable robots to accurately recognize and interact with partially occluded objects—a critical challenge for autonomous systems operating in human spaces. Silman’s most notable contribution is the Verified Partial Object Detector (VPOD), a novel algorithm introduced in their 2013 paper that extends Viewpoint Feature Histograms (VFH) to detect furniture and other objects even when they are heavily obstructed. This work, validated on real sensor data from a robot platform, addresses a fundamental limitation in 3D point cloud processing, where occlusion often causes detection failures. While the paper has garnered 2 citations, its significance lies in laying groundwork for robust robotic perception in dynamic settings. Silman’s research bridges the gap between theoretical computer vision and practical robotics, offering solutions that improve how machines understand and navigate the physical world. Their contributions are particularly relevant for applications in service robotics, autonomous navigation, and human-robot interaction.
Research Focus
Key Achievements
Top Papers
- 1Detecting partially occluded objects via segmentation and validation2 citations · 2013