We are analyzing https://link.springer.com/chapter/10.1007/978-3-642-33715-4_54.

Title:
Indoor Segmentation and Support Inference from RGBD Images | SpringerLink
Description:
We present an approach to interpret the major surfaces, objects, and support relations of an indoor scene from an RGBD image. Most existing work ignores physical interactions or is applied only to tidy rooms and hallways. Our goal is to parse typical, often messy,...
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Keywords {🔍}

google, scholar, eccv, indoor, computer, support, image, chapter, springer, heidelberg, hoiem, vision, rgbd, part, science, scene, scenes, hebert, eds, vol, privacy, cookies, information, publish, research, conference, paper, segmentation, lncs, content, search, inference, images, silberman, fergus, single, efros, iccv, daniilidis, maragos, paragios, university, usa, data, journal, kohli, surfaces, relations, rooms, object,

Topics {✒️}

indoor scene segmentation indoor 3d modeling privacy choices/manage cookies main content log infer support relations paper cite recover support relationships integer programming formulation inferred support lead dense depth maps structured light sensor consumer depth cameras ultrametric contour maps 3d scene cues computer vision 3d scene geometry paper silberman conditions privacy policy structured 3d interpretation 3d point clouds european economic area blocks world revisited accepting optional cookies indoor segmentation estimating spatial layout recovering occlusion boundaries indoor scene european conference nathan silberman semantic segmentation conference series computer science journal finder publish permissions reprints indoor scenes single image derek hoiem support relations 1449 rgbd images object segmentation rgbd images rgbd image privacy policy personal data books a semantic labeling industrial science 3d cues optional cookies manage preferences

Schema {🗺️}

ScholarlyArticle:
      headline:Indoor Segmentation and Support Inference from RGBD Images
      pageEnd:760
      pageStart:746
      image:https://media.springernature.com/w153/springer-static/cover/book/978-3-642-33715-4.jpg
      genre:
         Computer Science
         Computer Science (R0)
      isPartOf:
         name:Computer Vision – ECCV 2012
         isbn:
            978-3-642-33715-4
            978-3-642-33714-7
         type:Book
      publisher:
         name:Springer Berlin Heidelberg
         logo:
            url:https://www.springernature.com/app-sn/public/images/logo-springernature.png
            type:ImageObject
         type:Organization
      author:
            name:Nathan Silberman
            affiliation:
                  name:New York University
                  address:
                     name:Courant Institute, New York University, USA
                     type:PostalAddress
                  type:Organization
            type:Person
            name:Derek Hoiem
            affiliation:
                  name:University of Illinois at Urbana-Champaign
                  address:
                     name:Department of Computer Science, University of Illinois at Urbana-Champaign, USA
                     type:PostalAddress
                  type:Organization
            type:Person
            name:Pushmeet Kohli
            affiliation:
                  name:Microsoft Research
                  address:
                     name:Microsoft Research, Cambridge, UK
                     type:PostalAddress
                  type:Organization
            type:Person
            name:Rob Fergus
            affiliation:
                  name:New York University
                  address:
                     name:Courant Institute, New York University, USA
                     type:PostalAddress
                  type:Organization
            type:Person
      keywords:
      description:We present an approach to interpret the major surfaces, objects, and support relations of an indoor scene from an RGBD image. Most existing work ignores physical interactions or is applied only to tidy rooms and hallways. Our goal is to parse typical, often messy, indoor scenes into floor, walls, supporting surfaces, and object regions, and to recover support relationships. One of our main interests is to better understand how 3D cues can best inform a structured 3D interpretation. We also contribute a novel integer programming formulation to infer physical support relations. We offer a new dataset of 1449 RGBD images, capturing 464 diverse indoor scenes, with detailed annotations. Our experiments demonstrate our ability to infer support relations in complex scenes and verify that our 3D scene cues and inferred support lead to better object segmentation.
      datePublished:2012
      isAccessibleForFree:1
      context:https://schema.org
Book:
      name:Computer Vision – ECCV 2012
      isbn:
         978-3-642-33715-4
         978-3-642-33714-7
Organization:
      name:Springer Berlin Heidelberg
      logo:
         url:https://www.springernature.com/app-sn/public/images/logo-springernature.png
         type:ImageObject
      name:New York University
      address:
         name:Courant Institute, New York University, USA
         type:PostalAddress
      name:University of Illinois at Urbana-Champaign
      address:
         name:Department of Computer Science, University of Illinois at Urbana-Champaign, USA
         type:PostalAddress
      name:Microsoft Research
      address:
         name:Microsoft Research, Cambridge, UK
         type:PostalAddress
      name:New York University
      address:
         name:Courant Institute, New York University, USA
         type:PostalAddress
ImageObject:
      url:https://www.springernature.com/app-sn/public/images/logo-springernature.png
Person:
      name:Nathan Silberman
      affiliation:
            name:New York University
            address:
               name:Courant Institute, New York University, USA
               type:PostalAddress
            type:Organization
      name:Derek Hoiem
      affiliation:
            name:University of Illinois at Urbana-Champaign
            address:
               name:Department of Computer Science, University of Illinois at Urbana-Champaign, USA
               type:PostalAddress
            type:Organization
      name:Pushmeet Kohli
      affiliation:
            name:Microsoft Research
            address:
               name:Microsoft Research, Cambridge, UK
               type:PostalAddress
            type:Organization
      name:Rob Fergus
      affiliation:
            name:New York University
            address:
               name:Courant Institute, New York University, USA
               type:PostalAddress
            type:Organization
PostalAddress:
      name:Courant Institute, New York University, USA
      name:Department of Computer Science, University of Illinois at Urbana-Champaign, USA
      name:Microsoft Research, Cambridge, UK
      name:Courant Institute, New York University, USA

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