Controlling procedural modeling programs with stochastically-ordered sequential Monte Carlo
Abstract
We present a method for controlling the output of procedural modeling programs using Sequential Monte Carlo (SMC). Previous probabilistic methods for controlling procedural models use Markov Chain Monte Carlo (MCMC), which receives control feedback only for completely-generated models. In contrast, SMC receives feedback incrementally on incomplete models, allowing it to reallocate computational resources and converge quickly. To handle the many possible sequentializations of a structured, recursive procedural modeling program, we develop and prove the correctness of a new SMC variant, Stochastically-Ordered Sequential Monte Carlo (SOSMC). We implement SOSMC for general-purpose programs using a new programming primitive: the stochastic future. Finally, we show that SOSMC reliably generates high-quality outputs for a variety of programs and control scoring functions. For small computational budgets, SOSMC's outputs often score nearly twice as high as those of MCMC or normal SMC.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jul 27, 2015
- Source ID
- 10.1145/2766895
Entities
People
- Ben Mildenhall
- Daniel Ritchie
- Noah D. Goodman
- Pat Hanrahan
Organizations
- Defense Advanced Research Projects Agency
- Stanford University