Martial Hebert

About

Martial Hebert is not mentioned in the provided page text, and there is no information about his research focus, background, or key contributions in the content given.

Research topics

• Computer Science
• Artificial Intelligence
• Mathematical optimization
• Computer vision

Selected publications

• MAPPER: Multi-Agent Path Planning with Evolutionary Reinforcement Learning in Mixed Dynamic Environments

  2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · 2020 · 112 citations

  Multi-agent navigation in dynamic environments is of great industrial value when deploying a large scale fleet of robot to real-world applications. This paper proposes a decentralized partially observable multi-agent path planning with evolutionary reinforcement learning (MAPPER) method to learn an effective local planning policy in mixed dynamic environments. Reinforcement learning-based methods usually suffer performance degradation on long-horizon tasks with goal-conditioned sparse rewards, s…

  DOI

• Discovering Objects that Can Move

  2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · 2022 · 32 citations

  Senior authorCorresponding

  This paper studies the problem of object discovery - separating objects from the background without manual labels. Existing approaches utilize appearance cues, such as color, texture, and location, to group pixels into object-like regions. However, by relying on appearance alone, these methods fail to separate objects from the background in cluttered scenes. This is a fundamental limitation since the definition of an object is inherently ambiguous and context-dependent. To resolve this ambiguity…

  DOI

• Flexible Techniques for Differentiable Rendering with 3D Gaussians

  arXiv (Cornell University) · 2023-08-28 · 12 citations

  preprintOpen accessSenior author

  Fast, reliable shape reconstruction is an essential ingredient in many computer vision applications. Neural Radiance Fields demonstrated that photorealistic novel view synthesis is within reach, but was gated by performance requirements for fast reconstruction of real scenes and objects. Several recent approaches have built on alternative shape representations, in particular, 3D Gaussians. We develop extensions to these renderers, such as integrating differentiable optical flow, exporting watert…

  PublisherOA PDFDOI

• Object Discovery from Motion-Guided Tokens

  2023-06-01 · 10 citations

  articleSenior author

  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. Although both have been separately investigated, we introduce…

  PublisherDOI

• Beyond RGB: Scene-Property Synthesis with Neural Radiance Fields

  2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) · 2023-01-01 · 10 citations

  article

  Comprehensive 3D scene understanding, both geometrically and semantically, is important for real-world applications such as robot perception. Most of the existing work has focused on developing data-driven discriminative models for scene understanding. This paper provides a new approach to scene understanding, from a synthesis model perspective, by leveraging the recent progress on implicit scene representation and neural rendering. Building upon the great success of Neural Radiance Fields (NeRF…

  PublisherDOI

Recent grants

• RI: Detecting Boundaries for Segmentation and Recognition

  NSF · $324k · 2007–2011

• NRI-Large: Collaborative Research: Purposeful Prediction: Co-robot Interaction via Understanding Intent and Goals

  NSF · $2.2M · 2012–2017

Frequent coauthors

• Jean Ponce

  Département d'Informatique

  98 shared

• J. Andrew Bagnell

  66 shared

• Gerhard Goos

  RWTH Aachen University

  49 shared

• Jan Van Leeuwen

  Netherlands Institute for Radio Astronomy

  49 shared

• Andrew Zisserman

  49 shared

• Yu-Xiong Wang

  36 shared

• Takeo Kanade

  33 shared

• Katsushi Ikeuchi

  30 shared

Labs

• Human-Computer Interaction Institute (HCII)PI

Similar researchers at Carnegie Mellon University

• Russell Schwartzh-37