3D Shape Reconstruction from Sketches via Multi-view Convolutional Networks

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Authors Rui Wang, Evangelos Kalogerakis, Subhransu Maji, Matheus Gadelha, Zhaoliang Lun
Journal/Conference Name Proceedings - 2017 International Conference on 3D Vision, 3DV 2017
Paper Category
Paper Abstract We propose a method for reconstructing 3D shapes from 2D sketches in the form of line drawings. Our method takes as input a single sketch, or multiple sketches, and outputs a dense point cloud representing a 3D reconstruction of the input sketch(es). The point cloud is then converted into a polygon mesh. At the heart of our method lies a deep, encoder-decoder network. The encoder converts the sketch into a compact representation encoding shape information. The decoder converts this representation into depth and normal maps capturing the underlying surface from several output viewpoints. The multi-view maps are then consolidated into a 3D point cloud by solving an optimization problem that fuses depth and normals across all viewpoints. Based on our experiments, compared to other methods, such as volumetric networks, our architecture offers several advantages, including more faithful reconstruction, higher output surface resolution, better preservation of topology and shape structure.
Date of publication 2017
Code Programming Language Python
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