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- W3209708319 abstract "The point cloud is usually sparse and incomplete in reality, and the missing region of the point cloud is coherent when it is blocked by other objects. To tackle this problem, we propose a novel light-weight unsupervised model, namely the Slice Sequential Network, for point cloud completion. Our method only generates the missing parts with high fidelity, while many previous methods output the entire point cloud and leave out some important details. Specifically, we slice the incomplete point cloud and force the model to exploit the information lying between slices. In addition, we design a new algorithm for extracting geometric information, which can extract multi-scale features of points of the point cloud to enhance the use of slices. The qualitative and quantitative experiments show that our method is more lightweight and has better performance than the existing state-of-the-art methods." @default.
- W3209708319 created "2021-11-08" @default.
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- W3209708319 date "2021-01-01" @default.
- W3209708319 modified "2023-09-24" @default.
- W3209708319 title "Slice Sequential Network: A Lightweight Unsupervised Point Cloud Completion Network" @default.
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- W3209708319 doi "https://doi.org/10.1007/978-3-030-88007-1_9" @default.
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