Alan Sullivan

Alan Sullivan
  • Biography

    First at U.C. Berkeley, then at Lawrence Livermore National Laboratory, Alan studied interactions between ultra-high intensity femtosecond lasers and plasmas. Prior to joining MERL in 2007, he worked at a series of start-ups where he developed a novel volumetric 3D display technology. At MERL His research interests include computational geometry and computer graphics.

  • Recent News & Events

    •  TALK    [MERL Seminar Series 2022] Prof. Vincent Sitzmann presents talk titled Self-Supervised Scene Representation Learning
      Date & Time: Wednesday, March 30, 2022; 11:00 AM EDT
      Speaker: Vincent Sitzmann, MIT
      MERL Host: Alan Sullivan
      Research Areas: Artificial Intelligence, Computer Vision, Machine Learning
      Abstract
      • Given only a single picture, people are capable of inferring a mental representation that encodes rich information about the underlying 3D scene. We acquire this skill not through massive labeled datasets of 3D scenes, but through self-supervised observation and interaction. Building machines that can infer similarly rich neural scene representations is critical if they are to one day parallel people’s ability to understand, navigate, and interact with their surroundings. This poses a unique set of challenges that sets neural scene representations apart from conventional representations of 3D scenes: Rendering and processing operations need to be differentiable, and the type of information they encode is unknown a priori, requiring them to be extraordinarily flexible. At the same time, training them without ground-truth 3D supervision is an underdetermined problem, highlighting the need for structure and inductive biases without which models converge to spurious explanations.

        I will demonstrate how we can equip neural networks with inductive biases that enables them to learn 3D geometry, appearance, and even semantic information, self-supervised only from posed images. I will show how this approach unlocks the learning of priors, enabling 3D reconstruction from only a single posed 2D image, and how we may extend these representations to other modalities such as sound. I will then discuss recent work on learning the neural rendering operator to make rendering and training fast, and how this speed-up enables us to learn object-centric neural scene representations, learning to decompose 3D scenes into objects, given only images. Finally, I will talk about a recent application of self-supervised scene representation learning in robotic manipulation, where it enables us to learn to manipulate classes of objects in unseen poses from only a handful of human demonstrations.
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    •  NEWS    MERL work on scene-aware interaction featured in IEEE Spectrum
      Date: March 1, 2022
      MERL Contacts: Anoop Cherian; Chiori Hori; Jonathan Le Roux; Tim K. Marks; Alan Sullivan; Anthony Vetro
      Research Areas: Artificial Intelligence, Computer Vision, Machine Learning, Speech & Audio
      Brief
      • MERL's research on scene-aware interaction was recently featured in an IEEE Spectrum article. The article, titled "At Last, A Self-Driving Car That Can Explain Itself" and authored by MERL Senior Principal Research Scientist Chiori Hori and MERL Director Anthony Vetro, gives an overview of MERL's efforts towards developing a system that can analyze multimodal sensing information for highly natural and intuitive interaction with humans through context-dependent generation of natural language. The technology recognizes contextual objects and events based on multimodal sensing information, such as images and video captured with cameras, audio information recorded with microphones, and localization information measured with LiDAR.

        Scene-Aware Interaction for car navigation, one target application that the article focuses on, will provide drivers with intuitive route guidance. Scene-Aware Interaction technology is expected to have wide applicability, including human-machine interfaces for in-vehicle infotainment, interaction with service robots in building and factory automation systems, systems that monitor the health and well-being of people, surveillance systems that interpret complex scenes for humans and encourage social distancing, support for touchless operation of equipment in public areas, and much more. MERL's Scene-Aware Interaction Technology had previously been featured in a Mitsubishi Electric Corporation Press Release.

        IEEE Spectrum is the flagship magazine and website of the IEEE, the world’s largest professional organization devoted to engineering and the applied sciences. IEEE Spectrum has a circulation of over 400,000 engineers worldwide, making it one of the leading science and engineering magazines.
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  • Awards

    •  AWARD    R&D100 award for Deep Learning-based Water Detector
      Date: November 16, 2018
      Awarded to: Ziming Zhang, Alan Sullivan, Hideaki Maehara, Kenji Taira, Kazuo Sugimoto
      MERL Contact: Alan Sullivan
      Research Areas: Artificial Intelligence, Computer Vision, Machine Learning
      Brief
      • Researchers and developers from MERL, Mitsubishi Electric and Mitsubishi Electric Engineering (MEE) have been recognized with an R&D100 award for the development of a deep learning-based water detector. Automatic detection of water levels in rivers and streams is critical for early warning of flash flooding. Existing systems require a height gauge be placed in the river or stream, something that is costly and sometimes impossible. The new deep learning-based water detector uses only images from a video camera along with 3D measurements of the river valley to determine water levels and warn of potential flooding. The system is robust to lighting and weather conditions working well during the night as well as during fog or rain. Deep learning is a relatively new technique that uses neural networks and AI that are trained from real data to perform human-level recognition tasks. This work is powered by Mitsubishi Electric's Maisart AI technology.
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  • Research Highlights

  • MERL Publications

    •  Yu, X., van Baar, J., Chen, S., Sullivan, A., "Keypoint-aligned 3D Human Shape Recovery from A Single Imagewith Bilayer-Graph", International Conference on 3D Vision (3DV), DOI: 10.1109/​3DV53792.2021.00060, December 2021, pp. 505-514.
      BibTeX TR2021-143 PDF
      • @inproceedings{Yu2021dec,
      • author = {Yu, Xin and van Baar, Jeroen and Chen, Siheng and Sullivan, Alan},
      • title = {Keypoint-aligned 3D Human Shape Recovery from A Single Imagewith Bilayer-Graph},
      • booktitle = {International Conference on 3D Vision (3DV)},
      • year = 2021,
      • pages = {505--514},
      • month = dec,
      • doi = {10.1109/3DV53792.2021.00060},
      • url = {https://www.merl.com/publications/TR2021-143}
      • }
    •  Cherian, A., Pais, G., Jain, S., Marks, T.K., Sullivan, A., "InSeGAN: A Generative Approach to Segmenting Identical Instances in Depth Images", IEEE International Conference on Computer Vision (ICCV), October 2021, pp. 10023-10032.
      BibTeX TR2021-097 PDF Video Data Software Presentation
      • @inproceedings{Cherian2021oct,
      • author = {Cherian, Anoop and Pais, Goncalo and Jain, Siddarth and Marks, Tim K. and Sullivan, Alan},
      • title = {InSeGAN: A Generative Approach to Segmenting Identical Instances in Depth Images},
      • booktitle = {IEEE International Conference on Computer Vision (ICCV)},
      • year = 2021,
      • pages = {10023--10032},
      • month = oct,
      • publisher = {CVF},
      • url = {https://www.merl.com/publications/TR2021-097}
      • }
    •  Kannapiran, S., van Baar, J., Berman, S., "A Visual Inertial Odometry Framework for 3D Points, Lines and Planes", IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), DOI: 10.1109/​IROS51168.2021.9636526, September 2021.
      BibTeX TR2021-131 PDF
      • @inproceedings{Kannapiran2021sep,
      • author = {Kannapiran, Shenbagaraj and van Baar, Jeroen and Berman, Spring},
      • title = {A Visual Inertial Odometry Framework for 3D Points, Lines and Planes},
      • booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
      • year = 2021,
      • month = sep,
      • doi = {10.1109/IROS51168.2021.9636526},
      • url = {https://www.merl.com/publications/TR2021-131}
      • }
    •  Chen, S., Eldar, Y., "Graph Signaling Denoising via Unrolling Networks", IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), DOI: 10.1109/​ICASSP39728.2021.9415073, June 2021.
      BibTeX TR2021-071 PDF
      • @inproceedings{Chen2021jun3,
      • author = {Chen, Siheng and Eldar, Yonina},
      • title = {Graph Signaling Denoising via Unrolling Networks},
      • booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
      • year = 2021,
      • month = jun,
      • doi = {10.1109/ICASSP39728.2021.9415073},
      • url = {https://www.merl.com/publications/TR2021-071}
      • }
    •  Chen, S., Eldar, Y., "Time-Varying Graph Signal Inpainting via Unrolling Networks", IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), DOI: 10.1109/​ICASSP39728.2021.9413406, June 2021.
      BibTeX TR2021-070 PDF
      • @inproceedings{Chen2021jun,
      • author = {Chen, Siheng and Eldar, Yonina},
      • title = {Time-Varying Graph Signal Inpainting via Unrolling Networks},
      • booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
      • year = 2021,
      • month = jun,
      • doi = {10.1109/ICASSP39728.2021.9413406},
      • url = {https://www.merl.com/publications/TR2021-070}
      • }
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  • Software Downloads

  • Videos

  • MERL Issued Patents

    • Title: "System and Method for Distributed Machining Simulation"
      Inventors: Sullivan, Alan; Lee, Teng-Yok; Thornton, Jay E.
      Patent No.: 10,353,352
      Issue Date: Jul 16, 2019
    • Title: "System and Method for Determining Feedrates of Machining Tools"
      Inventors: Erdim, Huseyin; Sullivan, Alan
      Patent No.: 9,892,215
      Issue Date: Feb 13, 2018
    • Title: "Method and System for Rendering 3D Distance Fields"
      Inventors: Frisken, Sarah F.; Perry, Ronald N.; Sullivan, Alan
      Patent No.: 9,336,624
      Issue Date: May 10, 2016
    • Title: "System and Method for Performing Undo and Redo Operations during Machining Simulation"
      Inventors: Sullivan, Alan; Konobrytskyi, Dmytro
      Patent No.: 9,304,508
      Issue Date: Apr 5, 2016
    • Title: "Hybrid Adaptively Sampled Distance Fields"
      Inventors: Sullivan, Alan
      Patent No.: 9,122,270
      Issue Date: Sep 1, 2015
    • Title: "Analyzing Volume Removed During Machining Simulation"
      Inventors: Erdim, Huseyin; Sullivan, Alan
      Patent No.: 8,935,138
      Issue Date: Jan 13, 2015
    • Title: "System and Method for Simulating Machining Objects"
      Inventors: Sullivan, Alan; Manukyan, Liana
      Patent No.: 8,838,419
      Issue Date: Sep 16, 2014
    • Title: "System and Method for Identifying Defects of Surfaces Due to Machining Processes"
      Inventors: Sullivan, Alan; Yoganandan, Arun R
      Patent No.: 8,532,812
      Issue Date: Sep 10, 2013
    • Title: "System and Method for Optimizing Machining Simulation"
      Inventors: Sullivan, Alan; Yerazunis, William S.
      Patent No.: 8,483,858
      Issue Date: Jul 9, 2013
    • Title: "Volume-Based Coverage Analysis for Sensor Placement in 3D Environments"
      Inventors: Sullivan, Alan; Garaas, Tyler W
      Patent No.: 8,442,306
      Issue Date: May 14, 2013
    • Title: "A Method for Reconstructing a Distance Field of a Swept Volume at a Sample Point"
      Inventors: Frisken, Sarah F.; Perry, Ronald N.; Sullivan, Alan
      Patent No.: 8,265,909
      Issue Date: Sep 11, 2012
    • Title: "A Method for Simulating Numerically Controlled Milling Using Adaptively Sampled Distance Fields"
      Inventors: Frisken, Sarah F.; Perry, Ronald N.; Sullivan, Alan
      Patent No.: 8,010,328
      Issue Date: Aug 30, 2011
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