Example-based synthesis of 3D object arrangements

Matthew Fisher, Daniel Ritchie, Manolis Savva, Thomas Funkhouser, Pat Hanrahan

Research output: Contribution to journalArticlepeer-review

258 Scopus citations


We present a method for synthesizing 3D object arrangements from examples. Given a few user-provided examples, our system can synthesize a diverse set of plausible new scenes by learning from a larger scene database. We rely on three novel contributions. First, we introduce a probabilistic model for scenes based on Bayesian networks and Gaussian mixtures that can be trained from a small number of input examples. Second, we develop a clustering algorithm that groups objects occurring in a database of scenes according to their local scene neighborhoods. These contextual categories allow the synthesis process to treat a wider variety of objects as interchangeable. Third, we train our probabilistic model on a mix of user-provided examples and relevant scenes retrieved from the database. This mixed model learning process can be controlled to introduce additional variety into the synthesized scenes. We evaluate our algorithm through qualitative results and a perceptual study in which participants judged synthesized scenes to be highly plausible, as compared to hand-created scenes.

Original languageEnglish (US)
Article number135
JournalACM Transactions on Graphics
Issue number6
StatePublished - Nov 2012

All Science Journal Classification (ASJC) codes

  • Computer Graphics and Computer-Aided Design


  • 3D scenes
  • Automatic layout
  • Data-driven methods
  • Probabilistic modeling
  • Procedural modeling


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