Marcus Dutton
Papers
1
Total Citations
2
H-Index
1
About
Marcus Dutton is a robotics researcher whose work centers on 3D perception, object detection, and scene understanding for autonomous systems. His key contributions lie in developing algorithms that enable robots to robustly interpret cluttered, real-world environments. Dutton’s most cited work introduces the Verified Partial Object Detector (VPOD), a novel algorithm for detecting partially occluded objects—such as furniture—in 3D point clouds. By extending Viewpoint Feature Histograms (VFH), VPOD combines segmentation with a validation step to improve detection accuracy under challenging occlusion conditions. The algorithm was implemented and validated on real sensor data from a robot, demonstrating practical applicability in autonomous navigation and manipulation. Though early in his career, with his seminal paper accumulating over 2 citations, Dutton’s work addresses a critical bottleneck in robotic perception: handling occlusion in unstructured spaces. His research bridges the gap between theoretical computer vision and deployable robotics, making him a promising voice in the field of 3D scene analysis.
Research Focus
Key Achievements
Top Papers
- 1Detecting partially occluded objects via segmentation and validation2 citations · 2013