Reproducing biological vision in a machine is a challenging problem for which scientists have just scratched the surface. Living organisms are able to per- form complex tasks in an awestruckly efficient manner. The stereovision is one of these complex mechanisms that computer scientists try to replicate with high resolution cameras. This thesis takes on the stereovision problem in a neuromorphic way by mean of a new generation of vision sensors also called ”silicon retinas”. These silicon retinas mimic biological retinas by cap- turing the visual information into the form of asynchronous stream of events that encode contrast change at high temporal precision.


These sensors are used to study the importance of the precise timing and the scene temporal dynamics in solving the stereo correspondence problem. We propose one of the first 3D reconstruction methods which is able to produce 3D models in a truly event-based and asynchronous manner, from event-based visual information. Besides the novelty of proposing a truly temporal- based asynchronous event-driven approach of 3D reconstructions, this work is also able to preserve the native dynamic of the scene.

Time as information medium is proven to have a critical role in stereovision. Time can supplement, compensate and even replace the usual luminance and spatial information. This work lays strong foundations for future research on high temporal and event-based dynamic stereo vision. It also opens new promising perspectives for solving traditional machine vision problems thanks to the use of the new asynchronous vision paradigm.

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