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Deep Auxiliary Learning for Point Cloud Generation
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DOI：10.1109/ACCESS.2020.2968489.png)
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Abstract
En 中文
Generation point cloud from single image is a classical problem in computer vision. The learning methods for this task often adopt local distance metrics as loss function, which means the generated points are not easy to meet the overall shape distribution of the target object. To solve this problem, we introduce a voxel reconstruction network with distribution fitting as auxiliary task and propose a novel framework named Voxel-Assisted Points Generation Network(VAPGN). The auxiliary learning with voxel generation makes it easier to capture the shape distribution of objects in the image during the encoder phase, thereby effectively improving the result of point cloud reconstruction. To meet the needs of mobile and embedded applications, a mobile version of the model is also proposed. In the experiments, we verify the feasibility of our network on the ShapeNet dataset. The proposed framework has achieved outstanding performance on the point cloud generation task, comparing with various state-of-the-art methods.
Keywords:
Point cloud generation
auxiliary learning
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