Interactive furniture layout using interior design guidelines

By Paul C. Merrell, Eric Schkufza, Zeyang Li, Maneesh Agrawala, Vladlen Koltun. ACM Transactions on Graphics, 2011

448 citations recorded by the source corpus.

Subfields: Building and Construction, Control and Systems Engineering, Social Psychology

View Paper: https://www.semanticscholar.org/paper/62e73114aa597af4d105cf1966d0ad35eb73446c

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In Control and Systems Engineering

In Social Psychology

Citations in the corpus

Rendering synthetic objects into legacy photographs (2011, methodology): “[Merrell et al. 2011; Yu et al. 2011]) could also provide a more realistic sense of interaction and visualization (as demonstrated by Fig 18).”
SemanticPaint (2015, background): “Such augmented reality scenarios could be expanded for planning the renovation of a building, automating inventory, and designing interiors [Merrell et al. 2011].”
SceneFormer: Indoor Scene Generation with Transformers (2020, methodology): “Other methods place objects with a Bayesian model [19], or model object properties with probability distributions learned from the data [14, 22].”
Semantically-enriched 3D models for common-sense knowledge (2015, unknown): “The object attributes that we collected are critical for enabling automatic scene layout and scene synthesis applications explored by much prior work [18, 26, 7].”
Example-based synthesis of 3D object arrangements (2012, methodology): “Previous work addressing furniture layout used predefined sets of basic functional categories, such as “chairs” [Merrell et al. 2011; Yu et al. 2011].”
SceneNet: An annotated model generator for indoor scene understanding (2016, methodology): “We use a slight variation of the pairwise term used in [23] where M is the maximum recommended distance.”
IM2CAD (2016, unknown): “Researchers have explored a variety of techniques to automatically compute CAD scene models using non-photographic means, e.g., using example based approaches [11], utilizing text descriptions [7], and optimizing for furniture arrangements in a given space [47, 28].”
Understanding RealWorld Indoor Scenes with Synthetic Data (2015, methodology): “Various constraints and the notations for the associated weights and functions are summarised in Table 3.”
An interactive approach to semantic modeling of indoor scenes with an RGBD camera (2012, background): “Such 3D scenes can be used in applications for many purposes, such as providing an interface for user interaction [Izadi et al. 2011] and rearranging furniture for interior design [Yu et al. 2011; Merrell et al. 2011].”