Planit cabinet vision solid 2012 r2 user guide pdf

Given a room with a specified size, our method automatically generates 3D objects in a room from a randomly sampled latent code. We present a method for creating 3D indoor scenes with a generative model learned from a collection of semantic-segmented depth images captured from different unknown scenes. These graphs allow the system to support applications such as scene synthesis from a partial graph provided by a user.

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. 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. In the planning phase, it uses a deep graph convolutional generative model to synthesize relation graphs.

PlanIT represents the 'plan' for a scene via a relation graph, encoding objects as nodes and spatial/semantic relationships between objects as edges. With this in mind, we present PlanIT, a layout-generation framework that divides the problem into two distinct planning and instantiation phases. 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. 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). We present a new framework for interior scene synthesis that combines a high-level relation graph representation with spatial prior neural networks.

Planit cabinet vision solid 2012 r2 user guide pdf