Conference 3 Dec - 6 Dec Exhibition 4 Dec - 6 Dec


    Technical Papers

    01 Full Conference1 - Full Conference One Day



    Scenes, Syntax, Statistics and Semantics

    Friday, 05 December

    16:15 - 18:00

    Jasmine Hall

    Automatic Semantic Modeling of Indoor Scenes from Low-quality RGB-D Data Using Contextual Information

    We present a novel solution to automatic semantic modeling of indoor scenes from a sparse set of low-quality RGB-D images captured by a Microsoft Kinect camera.

    Kang Chen, Tsinghua University
    Yu-Kun Lai, Cardiff University
    Yu-Xin Wu, Tsinghua University
    Ralph Martin, Cardiff University
    Shi-Min Hu, Tsinghua University

    Imagining the Unseen: Stability-based Cuboid Arrangements for Scene Understanding

    A physical stability based approach to inferring the actual arrangement of indoor scenes by abstracting the scenes as collections of cuboids and hallucinating geometry in occluded regions, to help understand cluttered indoor scenes.

    Tianjia Shao, Zhejiang University
    Aron Monszpart, University College London
    Youyi Zheng, Yale University
    Bongjin Koo, University College London
    Weiwei Xu, Hangzhou Normal University
    Kun Zhou, Zhejiang University
    Niloy Mitra, University College London

    Structure Completion for Facade Layouts

    We present a method to complete missing structures in facade layouts. Our solution is to break the problem into two components: a statistical model to evaluate layouts and a planning algorithm to generate candidate layouts. This ensures the completed result is consistent with the observation and the layouts in database.

    Lubin Fan, Zhejiang University
    Przemyslaw Musialski, Vienna University of Technology
    Ligang Liu, University of Science and Technology of China
    Peter Wonka, King Abdullah University of Science and Technology

    Creating Consistent Scene Graphs Using a Probabilistic Grammar

    We develop algorithms that infer consistent segmentations, category labels and functional grouping via parsing with a probabilistic grammar learned from examples. In particular, we explicitly learn and leverage the hierarchical structure of scenes. Our experiments demonstrate that hierarchical analysis infers more accurate object labels than alternative methods.

    Tianqiang Liu, Princeton University
    Siddhartha Chaudhuri, Princeton University
    Vladimir Kim, Stanford University
    Qixing Huang, Stanford University
    Niloy Mitra, University College London
    Thomas Funkhouser, Princeton University

    SceneGrok: Inferring Action Maps in 3D Environments

    We present a method to predict the likelihood of a given action taking place over all locations in a 3D environment and refer to this representation as an action map over the scene. We demonstrate prediction of action maps in both 3D scans and virtual scenes.

    Manolis Savva, Stanford University
    Angel Chang, Stanford University
    Pat Hanrahan, Stanford University
    Matthew Fisher, Stanford University
    Matthias Niessner, Stanford University

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