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• W3126935866 abstract "In this paper, we introduce a novel conditional generative adversarial network that creates dense 3D point clouds, with color, for assorted classes of objects in an unsupervised manner. To overcome the difficulty of capturing intricate details at high resolutions, we propose a point transformer that progressively grows the network through the use of graph convolutions. The network is composed of a leaf output layer and an initial set of branches. Every training iteration evolves a point vector into a point cloud of increasing resolution. After a fixed number of iterations, the number of branches is increased by replicating the last branch. Experimental results show that our network is capable of learning and mimicking a 3D data distribution, and produces colored point clouds with fine details at multiple resolutions." @default.
• W3126935866 created "2021-02-15" @default.
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• W3126935866 date "2020-11-01" @default.
• W3126935866 modified "2023-10-16" @default.
• W3126935866 title "A Progressive Conditional Generative Adversarial Network for Generating Dense and Colored 3D Point Clouds" @default.
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• W3126935866 doi "https://doi.org/10.1109/3dv50981.2020.00081" @default.
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