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Martial Hebert - Carnegie Mellon University. Pittsburgh, PA, US

Martial Hebert

University Professor and Dean | Carnegie Mellon University


Martial Hebert performed research on interpreting 3D data from range sensors for obstacle detection and object recognition.


A native of France, Martial Hebert earned a doctorate in computer science at the University of Paris. He joined the Robotics Institute in 1984 — just five years after it was founded — and was named a full professor in 1999. Upon joining the CMU faculty, Hebert became part of the Autonomous Land Vehicles program, a precursor of today's research on self-driving vehicles. He performed research on interpreting 3D data from range sensors for obstacle detection, environment modeling and object recognition. For the next three decades, he led major research programs in autonomous systems, including ground and air vehicles, with contributions in the areas of perception for environment understanding and human interaction.

Hebert's research primarily centers on computer vision. He has led research on fundamental components, such as scene understanding, object recognition and applying machine learning to computer vision, as well as applications, which include systems that enable older adults and people with disabilities to live more independently. To help meet the needs of a rapidly expanding computer vision industry, he created the nation's first master's degree program in computer vision.

As director of the Robotics Institute, Hebert led an institution with more than 800 community members, including faculty, students and staff and colleagues at the National Robotics Engineering Center. During his tenure, the institute's operating budget reached an all-time high.

Hebert is a member of both the IEEE Robotics and Automation, and the IEEE Computer societies. Throughout his career, he has published hundreds of refereed papers in journals and conference proceedings, and has contributed to multiple edited volumes. He currently serves as editor-in-chief of the International Journal of Computer Vision. In 2022 he was named a University Professor, CMU's highest distinction for faculty members.

Areas of Expertise (5)


Autonomous Systems

Robotics and Autonomous Vehicles

Computer Vision

Interpretation of Perception Data

Media Appearances (5)

Computer vision researchers use motion to discover objects in videos

Tech Xplore  online


Martial Hebert, dean of CMU's School of Computer Science and a professor in the Robotics Institute, and robotics Ph.D. student Zhipeng Bao collaborated on the project with Toyota Research Institute, which sponsored the work. The research could help computers and robots better automatically detect objects in videos.

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Back to the future: Look to the 1980s for guidance on AI management

The Hill  online


Many have called to slow down the advancement of large language models, the kind of artificial intelligence (AI) that powers ChatGPT. But when it comes to AI development, we need to act now.

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Hebert, Hooker and Kraut Named University Professors

Carnegie Mellon University News  online


Three Carnegie Mellon University faculty members have been elevated to the rank of University Professor(opens in new window), the highest distinction a faculty member can achieve at CMU. The newly appointed University Professors are Martial Hebert, John Hooker and Robert E. Kraut.

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Hebert Installed as SCS Dean

Carnegie Mellon University News  online


It's fitting that the installation of School of Computer Science Dean Martial Hebert would begin with a musical intro from Carnegie Mellon University's robot bagpiper, McBlare. After all, Hebert spent his career at the Robotics Institute until taking the helm of SCS this past August.

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CMU names Martial Hebert Dean of School of Computer Science

The Robot Report  online


Martial Hebert, a leading researcher in computer vision and robotics, has been named dean of Carnegie Mellon University’s world-renowned School of Computer Science (SCS), effective August 15.

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Martial Hebert : Carnegie Mellon University : AI & Humanity Archive Tech Talk with Dr. Martial Hebert Martial Hebert : Director of the Robotics Institute at CMU SCS Attacking AI Challenges at the School of Computer Science CMU - It's Happening Here - Martial Hebert



Industry Expertise (3)



Computer Software

Education (3)

University of Paris: Doctorate, Computer Science

University of Paris: Maitrise de Mathématiques Appliquées

University of Paris: License de Mathématiques

Affiliations (3)

  • IEEE Robotics and Automation Society : Member
  • IEEE Computer Society : Member
  • International Journal of Computer Vision : Editor-in-Chief

Articles (5)

Object Discovery From Motion-Guided Tokens

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

2023 Object discovery -- separating objects from the background without manual labels -- is a fundamental open challenge in computer vision. Previous methods struggle to go beyond clustering of low-level cues, whether handcrafted (e.g., color, texture) or learned (e.g., from auto-encoders). In this work, we augment the auto-encoder representation learning framework with two key components: motion-guidance and mid-level feature tokenization.

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Learning Continuous Implicit Representation for Near-Periodic Patterns

European Conference on Computer Vision

2022 Near-Periodic Patterns (NPP) are ubiquitous in man-made scenes and are composed of tiled motifs with appearance differences caused by lighting, defects, or design elements. A good NPP representation is useful for many applications including image completion, segmentation, and geometric remapping.

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Approximate Differentiable Rendering with Algebraic Surfaces

European Conference on Computer Vision

2022 Differentiable renderers provide a direct mathematical link between an object’s 3D representation and images of that object. In this work, we develop an approximate differentiable renderer for a compact, interpretable representation, which we call Fuzzy Metaballs. Our approximate renderer focuses on rendering shapes via depth maps and silhouettes. It sacrifices fidelity for utility, producing fast runtimes and high-quality gradient information that can be used to solve vision tasks.

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Semantically supervised appearance decomposition for virtual staging from a single panorama

ACM Transactions on Graphics

2022 We describe a novel approach to decompose a single panorama of an empty indoor environment into four appearance components: specular, direct sunlight, diffuse and diffuse ambient without direct sunlight. Our system is weakly supervised by automatically generated semantic maps (with floor, wall, ceiling, lamp, window and door labels) that have shown success on perspective views and are trained for panoramas using transfer learning without any further annotations.

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Generative Modeling for Multi-task Visual Learning

Proceedings of the 39th International Conference on Machine Learning

2022 Generative modeling has recently shown great promise in computer vision, but it has mostly focused on synthesizing visually realistic images. In this paper, motivated by multi-task learning of shareable feature representations, we consider a novel problem of learning a shared generative model that is useful across various visual perception tasks.

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