Deep 3D Shape Reconstruction from Single-View Sketch Image
Fei Wang, Yu Yang, Baoquan Zhao, Junkun Jiang, Teng Zhou, Dazi Jiang, Tie Cai · 2020
In this paper, we introduce a novel 3D shape reconstruction method from single-view sketch image based on deep neural network. The proposed pipeline is mainly composed of three modules. The first module is sketch component segmentation based on multi-modal DNN fusion and is used to segment a given sketch into a series of basic units and build transformation template by the knots between them. The second module is a non-linear transformation network for multifarious sketch generation with the obtained transformation template. It creates the transformation representation of a sketch by extracting the shape features of an input sketch and transformation template samples. The third module is deep 3D shape reconstruction using multifarious sketches, which takes the obtianed sketches as input to reconstruct 3D shapes with a generative model. It fuses and optimizes features of multiple views and thus is more likely to generate high-quality 3D shapes. To evaluate the effectiveness of the proposed method, we conduct extensive experiments on a public 3D reconstruction dataset. The results demonstrate that our model can achieve better reconstruction performance than peer methods.