SceneSuggest: Context-driven 3D Scene Design

نویسندگان

  • Manolis Savva
  • Angel X. Chang
  • Maneesh Agrawala
چکیده

We present SCENESUGGEST: an interactive 3D scene design system providing context-driven suggestions for 3D model retrieval and placement. Using a point-and-click metaphor we specify regions in a scene in which to automatically place and orient relevant 3D models. Candidate models are ranked using a set of static support, position, and orientation priors learned from 3D scenes. We show that our suggestions enable rapid assembly of indoor scenes. We perform a user study comparing suggestions to manual search and selection, as well as to suggestions with no automatic orientation. We find that suggestions reduce total modeling time by 32%, that orientation priors reduce time spent re-orienting objects by 27%, and that context-driven suggestions reduce the number of text queries by 50%.

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عنوان ژورنال:
  • CoRR

دوره abs/1703.00061  شماره 

صفحات  -

تاریخ انتشار 2017