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ACM Transactions on Graphics· 2012Q1

Example-based synthesis of 3D object arrangements

Matthew Fisher, Daniel Ritchie, Manolis Savva, Thomas Funkhouser et al.

Short summary

A new method synthesizes diverse 3D object arrangements from a few user examples by learning a probabilistic model from a scene database.

AI-generated from the title and abstract; the full text is not read.

Key points

  • Synthesizes diverse 3D object arrangements from minimal user examples.
  • Employs a probabilistic model using Bayesian networks and Gaussian mixtures.
  • Uses a clustering algorithm for object interchangeability based on local scene neighborhoods.
  • Learns from a mix of user examples and a scene database for controlled variety.
  • Synthesized scenes were rated as highly plausible in perceptual studies.

AI-generated from the title and abstract; the full text is not read.

Abstract

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.

The authors' abstract, as published at the source. ACM Transactions on Graphics, 2012 · DOI ↗

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Field: Computer Graphics and Computer-Aided Design

Computer Graphics and Computer-Aided DesignComputer Science