World's Best Scientists 2026 revealed!
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Computer Science
USA
2026

D-Index & Metrics

Computer Science

D-Index
120
Citations
59208
World Ranking
146
National Ranking
85

Research.com Recognitions

  • 2026 - Research.com Computer Science in United States Leader Award
  • 2025 - Research.com Computer Science in United States Leader Award
  • 2023 - Research.com Computer Science in United States Leader Award
  • 2022 - Research.com Computer Science in United States Leader Award

Overview

Martial Hebert is affiliated with Carnegie Mellon University in the United States and has contributed extensively to research in computer science and engineering. Their work spans multiple subfields, primarily focusing on computer vision and pattern recognition, artificial intelligence, aerospace engineering, geology, and computational mechanics.

The scientist's research covers a range of specialized topics, including:

  • Advanced Vision and Imaging
  • Robotics and Sensor-Based Localization
  • Advanced Neural Network Applications
  • 3D Surveying and Cultural Heritage
  • Domain Adaptation and Few-Shot Learning
  • Video Surveillance and Tracking Methods
  • Multimodal Machine Learning Applications

Martial Hebert has published in various venues, with a significant number of papers appearing on arXiv (Cornell University). Other frequent publication venues include:

  • arXiv (Cornell University)
  • IEEE Transactions on Pattern Analysis and Machine Intelligence
  • International Journal of Computer Vision
  • 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)

Among recent papers authored under their supervision or collaboration are:

  • "Discovering Objects that Can Move" (2022), presented at the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • "Linear RGB-D SLAM for Structured Environments" (2021), published in IEEE Transactions on Pattern Analysis and Machine Intelligence
  • "Constrained Model-based Reinforcement Learning with Robust Cross-Entropy Method" (2020), available on arXiv (Cornell University)
  • "MAPPER: Multi-Agent Path Planning with Evolutionary Reinforcement Learning in Mixed Dynamic Environments" (2020), available on arXiv (Cornell University)
  • "Flexible Techniques for Differentiable Rendering with 3D Gaussians" (2023), available on arXiv (Cornell University)

Frequent collaborators include the following researchers with notable joint publications:

  • Yu-Xiong Wang
  • Zhipeng Bao
  • Pavel Tokmakov
  • Shuhong Zheng
  • Tiancheng Zhi

The scientist's research interests and output reflect a focus on the integration of machine learning with computer vision, robotics, and 3D imaging technologies. Publications often address both theoretical developments and practical applications in structured environments, autonomous agents, and advanced imaging techniques.

Best Publications

  • Using spin images for efficient object recognition in cluttered 3D scenes

    A.E. Johnson;M. Hebert

  • Autonomous driving in urban environments: Boss and the Urban Challenge

    Chris Urmson;Joshua Anhalt;Drew Bagnell;Christopher Baker

  • A spectral technique for correspondence problems using pairwise constraints

    M. Leordeanu;M. Hebert

  • Putting Objects in Perspective

    D. Hoiem;A.A. Efros;M. Hebert

  • Cross-Stitch Networks for Multi-task Learning

    Ishan Misra;Abhinav Shrivastava;Abhinav Gupta;Martial Hebert

  • Vision and navigation for the Carnegie-Mellon Navlab

    C. Thorpe;M.H. Hebert;T. Kanade;S.A. Shafer

  • The representation, recognition, and locating of 3-d objects

    O D Faugeras;M Hebert

  • Toward Objective Evaluation of Image Segmentation Algorithms

    R. Unnikrishnan;C. Pantofaru;M. Hebert

  • Recovering Surface Layout from an Image

    Derek Hoiem;Alexei A. Efros;Martial Hebert

  • Geometric context from a single image

    D. Hoiem;A.A. Efros;M. Hebert

  • Semi-Supervised Self-Training of Object Detection Models

    C. Rosenberg;M. Hebert;H. Schneiderman

  • Automatic photo pop-up

    Derek Hoiem;Alexei A. Efros;Martial Hebert

  • PCN: Point Completion Network

    Wentao Yuan;Tejas Khot;David Held;Christoph Mertz

  • Shuffle and Learn: Unsupervised Learning Using Temporal Order Verification

    Ishan Misra;C. Lawrence Zitnick;Martial Hebert

  • Simultaneous Localization, Mapping and Moving Object Tracking

    Chieh-Chih Wang;Charles Thorpe;Sebastian Thrun;Martial Hebert

  • Activity forecasting

    Kris M. Kitani;Brian D. Ziebart;James Andrew Bagnell;Martial Hebert

  • Low-Shot Learning from Imaginary Data

    Yu-Xiong Wang;Ross Girshick;Martial Hebert;Bharath Hariharan

  • Efficient visual event detection using volumetric features

    Yan Ke;R. Sukthankar;M. Hebert

  • An empirical study of context in object detection

    Santosh K Divvala;Derek Hoiem;James H Hays;Alexei A Efros

  • Discriminative random fields: a discriminative framework for contextual interaction in classification

    Sanjiv Kumar;Hebert

Frequent Co-Authors

J. Andrew Bagnell
J. Andrew Bagnell Carnegie Mellon University
Takeo Kanade
Takeo Kanade Carnegie Mellon University
Jean Ponce
Jean Ponce École Normale Supérieure
Anthony Stentz
Anthony Stentz Carnegie Mellon University
Katsushi Ikeuchi
Katsushi Ikeuchi Microsoft (United States)
Alexei A. Efros
Alexei A. Efros University of California, Berkeley
Abhinav Gupta
Abhinav Gupta Carnegie Mellon University
Rahul Sukthankar
Rahul Sukthankar Google (United States)
Daniel Huber
Daniel Huber University of Geneva
Cordelia Schmid
Cordelia Schmid French Institute for Research in Computer Science and Automation - INRIA

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