Our framework is capable of handling challenging scenes that includeįast motion and strong occlusions. Reconstruction algorithm has both high frame rate and well-recovered spatialįeatures. Residual denoiser to remove reconstruction artifacts. Residual errors in PBR reconstruction are further reduced by training a Stochastic gradient descent optimization using automatic differentiation. We use aĭifferentiable model to approximate the physical sensing process, which enables Reconstruction (PBR) process and a residual denoising process. We factor the reconstruction into a physical model-based Introduce an algorithm to recover a space-time video from these two modes of "events" which encode brightness variations over time at over 1000 fps. Ourįramework takes as inputs two types of data streams: an intensity frame streamĪnd a neuromorphic event stream, which consists of asynchronous bipolar This work, we present a computational high speed video synthesis framework. Video frame synthesis is an active computer vision problem which hasĪpplications in video compression, streaming, editing, and understanding.
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