Patrick Peerez

Papers

Total Citations

209

H-Index

About

Patrick Perez is a leading figure in computer vision and robotics, renowned for his pioneering work in 3D scene understanding and semantic reconstruction. His research bridges the gap between geometric perception and semantic interpretation, enabling machines to not only map their environment but also comprehend the objects within it. His seminal paper, "Incremental dense semantic stereo fusion for large-scale semantic scene reconstruction" (2015, 209 citations), introduced a groundbreaking framework that fuses stereo depth estimation with semantic segmentation in real-time. This work allows robots to build dense, semantically-labeled 3D maps of large-scale environments—a critical capability for autonomous navigation and interaction. By tackling the challenge of incremental fusion, Perez demonstrated how to maintain accuracy and efficiency even as scenes grow in complexity. His contributions have had a lasting impact on fields like augmented reality, autonomous driving, and service robotics, where understanding both "where" and "what" is essential. With over 200 citations on this key paper alone, Perez's research continues to inspire new approaches to holistic scene understanding, solidifying his reputation as a pioneer in semantic 3D reconstruction.