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• W2970800437 abstract "Point clouds are unstructured and unordered data, as opposed to images. Thus, most machine learning approach developed for image cannot be directly transferred to point clouds. In this paper, we propose a generalization of discrete convolutional neural networks (CNNs) in order to deal with point clouds by replacing discrete kernels by continuous ones. This formulation is simple, allows arbitrary point cloud sizes and can easily be used for designing neural networks similarly to 2D CNNs. We present experimental results with various architectures, highlighting the flexibility of the proposed approach. We obtain competitive results compared to the state-of-the-art on shape classification, part segmentation and semantic segmentation for large-scale point clouds." @default.
• W2970800437 created "2019-09-05" @default.
• W2970800437 creator A5065544923 @default.
• W2970800437 date "2019-04-04" @default.
• W2970800437 modified "2023-09-26" @default.
• W2970800437 title "ConvPoint: Continuous Convolutions for Point Cloud Processing" @default.
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• W2970800437 hasPublicationYear "2019" @default.
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