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This paper addresses the problem of estimating the depth map of a scene given a single RGB image. We propose a fully convolutional architecture, encompassing residual learning, to model the ambiguous mapping between monocular images and depth maps. In order to improve the output resolution, we present a novel way to efficiently learn feature map up-sampling within the network. For optimization, we...
This paper presents a review of the state-of-the-art techniques in the field of 3D invariant features for the automatic registration of point clouds and 3D meshes. The paper proposes also a multi-stage 3D registration pipeline implemented using the PCL libraries. Experiments are carried out on datasets related to heritage scenarios and addressing large-scale outdoor data acquisitions as well as small...
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