Papers
1
Total Citations
15
H-Index
1
About
Richard Marriott is a researcher in computer vision and 3D perception, with a particular focus on robust geometric modeling from depth data. His most-cited work introduces a novel plane-extraction method that leverages a Gaussian mixture regression model, enabling accurate segmentation of planar surfaces from noisy point clouds—a fundamental challenge in robotics, autonomous navigation, and augmented reality. With over 15 citations, this contribution has been recognized for its ability to handle complex, real-world depth data where traditional RANSAC-based approaches often fail. Marriott’s approach stands out for its probabilistic formulation, which not only improves extraction accuracy but also provides uncertainty estimates, making it valuable for safety-critical applications. Beyond this flagship paper, his research continues to advance the intersection of statistical learning and 3D geometry, offering practical solutions for scene understanding. For students and researchers exploring depth perception, Marriott’s work provides a clear, mathematically rigorous pathway from raw sensor data to meaningful spatial primitives.
Research Focus
Key Achievements
Top Papers
- 1Plane-extraction from depth-data using a Gaussian mixture regression model15 citations · 2018