Morten Lidegaard

Papers

Total Citations

209

H-Index

About

Morten Lidegaard is a leading researcher in computer vision and robotics, with a focus on dense 3D reconstruction and semantic scene understanding. His most influential work, "Incremental dense semantic stereo fusion for large-scale semantic scene reconstruction" (2015, 209 citations), addresses a critical challenge in robotics: enabling machines to perceive and interpret large-scale environments in real time. By fusing stereo depth data with semantic labels, Lidegaard developed a method that allows robots to not only reconstruct the 3D structure of a scene but also recognize and classify objects within it—mimicking human-like spatial awareness. This contribution has been foundational for advancing autonomous navigation, augmented reality, and robotic manipulation in complex, dynamic settings. His research bridges the gap between low-level geometry and high-level semantics, significantly improving how robots interact with unstructured environments. With over 200 citations on his seminal paper alone, Lidegaard’s work continues to influence a new generation of systems that require robust, real-time scene understanding, making him a key figure in the evolution of intelligent, perceptive machines.