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Kpconvx: Modernizing Kernel Point Convolution With Kernel Attention

In the field of deep point cloud understanding, KPConv is a unique architecture that uses kernel points to locate
Apple Machine Learning Research 4:05 pm on May 28, 2024

KPConvX advances kernel point convolution by incorporating Kernel Attention for improved performance in deep point cloud understanding, surpassing recent MLP networks. This approach leverages depthwise KPConvD and attention-augmented KPConv models with notable success on benchmark datasets like ScanObjectN, Scannet v1.5, and S3DIS. The research highlights the potential of kernel-attention integration in deep learning architectures for point cloud processing.

Advanced Kernel Point Convolution (KPConvX):
Integration of Kernel Attention to enhance convolutional performance.
Depthwise KPConvD and KPConv models, outperforming MLP counterparts.
Success on ScanObjectN, Scannet v1.5, and S3DIS benchmark datasets.
Bridging the gap between kernel point principals and modern architectures.

https://machinelearning.apple.com/research/kpconvx

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Kpconvx: Modernizing Kernel Point Convolution With Kernel Attention

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