![]() ![]() ![]() At test time, we fine-tune this network to satisfy the geometric constraints of a particular input video, while retaining its ability to synthesize plausible depth details in parts of the video that are less constrained. Unlike the ad-hoc priors in classical reconstruction, we use a learning-based prior, i.e., a convolutional neural network trained for single-image depth estimation. We leverage a conventional structure-from-motion reconstruction to establish geometric constraints on pixels in the video. We present an algorithm for reconstructing dense, geometrically consistent depth for all pixels in a monocular video. Consistent_depth - We estimate dense, flicker-free, geometrically consistent depth from monocular video, for example hand-held cell phone video ![]()
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