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Reconstruction of the tridimensional geometry of a visual scene using thebinocular disparity information is an important issue in computer vision andmobile robotics, which can be formulated as a Bayesian inference problem.However, computation of the full disparity distribution with an advancedBayesian model is usually an intractable problem, and proves computationallychallenging even with a simple model. In this paper, we show how probabilistichardware using distributed memory and alternate representation of data asstochastic bitstreams can solve that problem with high performance and energyefficiency. We put forward a way to express discrete probability distributionsusing stochastic data representations and perform Bayesian fusion using thoserepresentations, and show how that approach can be applied to diparitycomputation. We evaluate the system using a simulated stochastic implementationand discuss possible hardware implementations of such architectures and theirpotential for sensorimotor processing and robotics.

Quick and energy-efficient Bayesian computing of binocular disparity using stochastic digital signals