4.7 Article

PlanIT: Planning and Instantiating Indoor Scenes with Relation Graph and Spatial Prior Networks

期刊

ACM TRANSACTIONS ON GRAPHICS
卷 38, 期 4, 页码 -

出版社

ASSOC COMPUTING MACHINERY
DOI: 10.1145/3306346.3322941

关键词

indoor scene synthesis; object layout; neural networks; convolutional networks; deep learning; relationship graphs; graph generation

资金

  1. NSF [1753684]
  2. Technical University of Munich-Institute for Advanced Study - German Excellence Initiative
  3. European Union [291763]
  4. Direct For Computer & Info Scie & Enginr
  5. Div Of Information & Intelligent Systems [1753684] Funding Source: National Science Foundation

向作者/读者索取更多资源

We present a new framework for interior scene synthesis that combines a high-level relation graph representation with spatial prior neural networks. We observe that prior work on scene synthesis is divided into two camps: object-oriented approaches (which reason about the set of objects in a scene and their configurations) and space-oriented approaches (which reason about what objects occupy what regions of space). Our insight is that the object-oriented paradigm excels at high-level planning of how a room should be laid out, while the space-oriented paradigm performs well at instantiating a layout by placing objects in precise spatial configurations. With this in mind, we present PlanIT, a layout-generation framework that divides the problem into two distinct planning and instantiation phases. PlanIT represents the plan for a scene via a relation graph, encoding objects as nodes and spatial/semantic relationships between objects as edges. In the planning phase, it uses a deep graph convolutional generative model to synthesize relation graphs. In the instantiation phase, it uses image-based convolutional network modules to guide a search procedure that places objects into the scene in a manner consistent with the graph. By decomposing the problem in this way, PlanIT generates scenes of comparable quality to those generated by prior approaches (as judged by both people and learned classifiers), while also providing the modeling flexibility of the intermediate relationship graph representation. These graphs allow the system to support applications such as scene synthesis from a partial graph provided by a user.

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