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Structured Prediction of Unobserved Voxels from a Single Depth Image

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Original languageEnglish
Title of host publication2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages10
ISBN (Electronic)978-1-4673-8851-1
ISBN (Print)978-1-4673-8852-8
Publication statusPublished - 12 Dec 2016
Event29th IEEE Conference on Computer Vision and Pattern Recognition - Las Vegas, United States
Duration: 26 Jun 20161 Jul 2016

Publication series

ISSN (Electronic)1063-6919


Conference29th IEEE Conference on Computer Vision and Pattern Recognition
Abbreviated titleCVPR 2016
CountryUnited States
CityLas Vegas
Internet address


Building a complete 3D model of a scene, given only a single depth image, is underconstrained. To gain a full volumetric model, one needs either multiple views, or a single view together with a library of unambiguous 3D models that will fit the shape of each individual object in the scene.

We hypothesize that objects of dissimilar semantic classes often share similar 3D shape components, enabling a limited dataset to model the shape of a wide range of objects, and hence estimate their hidden geometry. Exploring this hypothesis, we propose an algorithm that can complete the unobserved geometry of tabletop-sized objects, based on a supervised model trained on already available volumetric elements. Our model maps from a local observation in a single depth image to an estimate of the surface shape in the surrounding neighborhood. We validate our approach both qualitatively and quantitatively on a range of indoor object collections and challenging real scenes.

    Research areas

  • computer graphics, geometry, shape recognition, structured prediction, unobserved voxels, single depth image, unambiguous 3D models, 3D shape components, tabletop-sized objects, supervised model, indoor object collections, challenging real scenes, Three-dimensional displays, Geometry, Shape, Solid modeling, Two dimensional displays, Semantics, Cameras


29th IEEE Conference on Computer Vision and Pattern Recognition


Las Vegas, United States

Event: Conference

ID: 122747449