Papers
8
Total Citations
47
H-Index
5
About
David Suter is a prominent researcher in computer vision and robotics, whose work spans model estimation, scene understanding, and robot manipulation. His early contributions to robust fitting and statistical methods for computer vision—including his highly cited work on simultaneously estimating the fundamental matrix and homographies—have provided foundational tools for multi-view geometry. Suter’s research has advanced assistive robotics, demonstrated by his work on robot grasping using stereo vision and SIFT-based object recognition, which has garnered over 10 citations. He has also made significant strides in affordance segmentation, exploring how deep networks can enable robots to understand object functionality from images. More recently, Suter has contributed to foundational models for segmentation, proposing the Segment Any Object Model (SAOM) for multi-class multi-instance tasks, and to embodied navigation with the StratXplore framework for vision-language navigation. His work on panoramic scene understanding, including the PanoSCU dataset, further underscores his impact on indoor robotics. With over 40 citations across his most-cited papers, Suter’s research continues to shape the intersection of computer vision, machine learning, and robotic perception.
Research Focus
Key Achievements
Top Papers
- 1
- 2Simultaneously Estimating the Fundamental Matrix and Homographies8 citations · 2009
- 3Learning Affordance Segmentation: An Investigative Study8 citations · 2020
- 4
- 5Robust Fitting Using Mean Shift: Applications in Computer Vision7 citations · 2004
- 6
- 7
- 8