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• W4309928662 abstract "In this paper, we propose GVA-net, a fully-convolutional architecture for point cloud classification and part-segmentation. We prove that the hierarchical of the receptive field among the permutation-constant neighborhood leads to better mean accuracy on the benchmark ModelNet40 dataset by 0.7pp with respect to the best method relying on local context aggregation — PointVGG. We proved that substituting a fully-connected MLP-based classifier with a convolution classifying module, followed by average pooling significantly reduces the complexity of the model without deterioration results. The code used in our study is open-source and publicly available in a repository under the MIT license at https://github.com/jamesWalczak/gva-net." @default.
• W4309928662 created "2022-11-30" @default.
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• W4309928662 date "2023-01-01" @default.
• W4309928662 modified "2023-09-26" @default.
• W4309928662 title "Ultrasmall fully-convolution GVA-net for point cloud processing" @default.
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• W4309928662 doi "https://doi.org/10.1016/j.asoc.2022.109837" @default.
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