Abstract:Predicting a convincing depth map from a monocular single image is a daunting task in the field of computer vision. In this paper, we propose a novel detail-preserving depth estimation (DPDE) algorithm based on a modified fully convolutional residual network and gradient network. Specifically, we first introduce a new deep network that combines the fully convolutional residual network (FCRN) and a U-shaped architecture to generate the global depth map. Meanwhile, an efficient feature similarity-based loss term… Show more
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