Learning shape placements by example

We present a method to learn and propagate shape placements in 2D polygonal scenes from a few examples provided by a user. The placement of a shape is modeled as an oriented bounding box. Simple geometric relationships between this bounding box and nearby scene polygons define a feature set for the placement. The feature sets of all example placements are then used to learn a probabilistic model over all possible placements and scenes. With this model, we can generate a new set of placements with similar geometric relationships in any given scene. We introduce extensions that enable propagation and generation of shapes in 3D scenes, as well as the application of a learned modeling session to large scenes without additional user interaction. These concepts allow us to generate complex scenes with thousands of objects with relatively little user interaction.

[1]  Jean Ponce,et al.  A Theoretical Analysis of Feature Pooling in Visual Recognition , 2010, ICML.

[2]  Leonidas J. Guibas,et al.  Probabilistic reasoning for assembly-based 3D modeling , 2011, ACM Trans. Graph..

[3]  Siddhartha Chaudhuri,et al.  A probabilistic model for component-based shape synthesis , 2012, ACM Trans. Graph..

[4]  Szymon Rusinkiewicz,et al.  Modeling by example , 2004, ACM Trans. Graph..

[5]  Pat Hanrahan,et al.  Example-based synthesis of 3D object arrangements , 2012, ACM Trans. Graph..

[6]  James Stewart,et al.  Constraint-Based Automatic Placement for Scene Composition , 2002, Graphics Interface.

[7]  Michael Wimmer,et al.  Edit propagation using geometric relationship functions , 2014, TOGS.

[8]  Daniel Cohen-Or,et al.  iWIRES: an analyze-and-edit approach to shape manipulation , 2009, ACM Trans. Graph..

[9]  John Hart,et al.  ACM Transactions on Graphics , 2004, SIGGRAPH 2004.

[10]  Daniel Cohen-Or,et al.  Component‐wise Controllers for Structure‐Preserving Shape Manipulation , 2011, Comput. Graph. Forum.

[11]  Zhifu Cui,et al.  An improved smoothed l0-norm algorithm based on multiparameter approximation function , 2010, 2010 IEEE 12th International Conference on Communication Technology.

[12]  Hans-Peter Seidel,et al.  An algebraic model for parameterized shape editing , 2012, ACM Trans. Graph..

[13]  Pat Hanrahan,et al.  Synthesizing open worlds with constraints using locally annealed reversible jump MCMC , 2012, ACM Trans. Graph..

[14]  Maneesh Agrawala,et al.  Interactive furniture layout using interior design guidelines , 2011, SIGGRAPH 2011.

[15]  Chi-Keung Tang,et al.  Make it home: automatic optimization of furniture arrangement , 2011, ACM Trans. Graph..

[16]  Pat Hanrahan,et al.  Characterizing structural relationships in scenes using graph kernels , 2011, SIGGRAPH 2011.

[17]  Azriel Rosenfeld,et al.  Gray-level corner detection , 1982, Pattern Recognit. Lett..

[18]  H. Kuhn The Hungarian method for the assignment problem , 1955 .

[19]  Pat Hanrahan,et al.  Context-based search for 3D models , 2010, ACM Trans. Graph..

[20]  Thomas Arildsen,et al.  Improving Smoothed l0 Norm in Compressive Sensing Using Adaptive Parameter Selection , 2012, ArXiv.

[21]  Christopher M. Bishop,et al.  Pattern Recognition and Machine Learning (Information Science and Statistics) , 2006 .