Journal Article10.1145/2461912.2461968
Sketch2Scene: sketch-based co-retrieval and co-placement of 3D models
Kun Xu,Kang Chen,Hongbo Fu,Weilun Sun,Shi-Min Hu +4 more
- 21 Jul 2013
- Vol. 32, Iss: 4, pp 123
220
TL;DR: Sketch2Scene, a framework that automatically turns a freehand sketch drawing inferring multiple scene objects to semantically valid, well arranged scenes of 3D models, is presented, promising to use as an alternative but more efficient tool of standard 3D modeling for 3D scene construction.
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Abstract: This work presents Sketch2Scene, a framework that automatically turns a freehand sketch drawing inferring multiple scene objects to semantically valid, well arranged scenes of 3D models. Unlike the existing works on sketch-based search and composition of 3D models, which typically process individual sketched objects one by one, our technique performs co-retrieval and co-placement of 3D relevant models by jointly processing the sketched objects. This is enabled by summarizing functional and spatial relationships among models in a large collection of 3D scenes as structural groups. Our technique greatly reduces the amount of user intervention needed for sketch-based modeling of 3D scenes and fits well into the traditional production pipeline involving concept design followed by 3D modeling. A pilot study indicates that it is promising to use our technique as an alternative but more efficient tool of standard 3D modeling for 3D scene construction.
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Citations
SceneNN: A Scene Meshes Dataset with aNNotations
Binh-Son Hua,Quang-Hieu Pham,Duc Thanh Nguyen,Minh-Khoi Tran,Lap-Fai Yu,Sai-Kit Yeung +5 more
- 01 Jan 2016
TL;DR: This paper introduces SceneNN, an RGB-D scene dataset consisting of 100 scenes that is used as a benchmark to evaluate the state-of-the-art methods on relevant research problems such as intrinsic decomposition and shape completion.
431
Deep convolutional priors for indoor scene synthesis
TL;DR: This work presents a convolutional neural network based approach for indoor scene synthesis that generates scenes that are preferred over the baselines, and in some cases are equally preferred to human-created scenes.
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PlanIT: planning and instantiating indoor scenes with relation graph and spatial prior networks
TL;DR: A new framework for interior scene synthesis that combines a high-level relation graph representation with spatial prior neural networks, and generates scenes of comparable quality to those generated by prior approaches, while also providing the modeling flexibility of the intermediate relationship graph representation.
3D Shape Reconstruction from Sketches via Multi-view Convolutional Networks
Zhaoliang Lun,Matheus Gadelha,Evangelos Kalogerakis,Subhransu Maji,Rui Wang +4 more
- 20 Jul 2017
TL;DR: In this article, a deep, encoder-decoder network is proposed to reconstruct 3D shapes from 2D sketches in the form of line drawings, where the encoder converts the sketch into a compact representation encoding shape information and the decoder converts this representation into depth and normal maps capturing the underlying surface from several output viewpoints.
233
GRAINS: Generative Recursive Autoencoders for INdoor Scenes
Manyi Li,Akshay Gadi Patil,Kai Xu,Siddhartha Chaudhuri,Owais Khan,Ariel Shamir,Changhe Tu,Baoquan Chen,Daniel Cohen-Or,Hao Zhang +9 more
TL;DR: A generative neural network which enables us to generate plausible 3D indoor scenes in large quantities and varieties, easily and highly efficiently, and shows applications of GRAINS including 3D scene modeling from 2D layouts, scene editing, and semantic scene segmentation via PointNet.
232
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