Philip David
Papers
10
Total Citations
361
H-Index
5
About
Philip David is a computer vision and robotics researcher whose work spans pose estimation, mobile robot navigation, and autonomous exploration. He is perhaps best known for co-developing **SoftPOSIT** (2004), a landmark algorithm that simultaneously determines the pose and point correspondences of a 3D object from a single 2D image — a notoriously difficult coupled problem. This contribution has garnered over 237 citations, establishing it as a foundational reference in model-to-image registration and object recognition literature. Beyond pose estimation, David has made significant contributions to urban robotics perception. His research on building facade detection, segmentation, and parameter estimation addresses how mobile robots can localize and navigate intelligently in complex outdoor environments, with applications in semantic scene understanding and autonomous guidance. He also pioneered work on stairway modeling from depth imagery, enabling robots to incorporate multi-floor navigation into broader path planning — a critical step toward truly autonomous indoor exploration. Earlier in his career, David contributed to military robotics, developing unmanned ground vehicles for reconnaissance and target acquisition in real Army training exercises. Across these diverse domains, his work consistently bridges rigorous computer vision methodology with practical robotic deployment, making him a notable contributor to applied autonomy research.
Research Focus
Key Achievements
Top Papers
- 1SoftPOSIT: Simultaneous Pose and Correspondence Determination237 citations · 2004
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- 5Orientation descriptors for localization in urban environments11 citations · 2011
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- 7Evaluation of the SoftPOSIT Model-to-Image Registration Algorithm4 citations · 2002
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