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- W2897529552 abstract "This paper introduces DensePoint, a densely sampled and annotated point cloud dataset containing over 10,000 single objects across 16 categories, by merging different kind of information from two existing datasets. Each point cloud in DensePoint contains 40,000 points, and each point is associated with two sorts of information: RGB value and part annotation. In addition, we propose a method for point cloud colorization by utilizing Generative Adversarial Networks (GANs). The network makes it possible to generate colours for point clouds of single objects by only giving the point cloud itself. Experiments on DensePoint show that there exist clear boundaries in point clouds between different parts of an object, suggesting that the proposed network is able to generate reasonably good colours. Our dataset is publicly available on the project page ( http://rwdc.nagao.nuie.nagoya-u.ac.jp/DensePoint )." @default.
- W2897529552 created "2018-10-26" @default.
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- W2897529552 date "2018-12-08" @default.
- W2897529552 modified "2023-10-18" @default.
- W2897529552 title "Point Cloud Colorization Based on Densely Annotated 3D Shape Dataset" @default.
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- W2897529552 doi "https://doi.org/10.1007/978-3-030-05710-7_36" @default.
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