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3D Scene Mesh from CNN Depth Predictions and Sparse Monocular SLAM
2017
2017 IEEE International Conference on Computer Vision Workshops (ICCVW)
In this paper, we propose a novel framework for integrating geometrical measurements of monocular visual simultaneous localization and mapping (SLAM) and depth prediction using a convolutional neural network (CNN). In our framework, SLAM-measured sparse features and CNNpredicted dense depth maps are fused to obtain a more accurate dense 3D reconstruction including scale. We continuously update an initial 3D mesh by integrating accurately tracked sparse features points. Compared to prior work on
doi:10.1109/iccvw.2017.112
dblp:conf/iccvw/MukasaXS17
fatcat:tdnufowpo5hb3lr4p32aueuxfq