A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit <a rel="external noopener" href="https://arxiv.org/pdf/2107.12655v1.pdf">the original URL</a>. The file type is <code>application/pdf</code>.
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Despite the remarkable success of deep learning, optimal convolution operation on point cloud remains indefinite due to its irregular data structure. In this paper, we present Cubic Kernel Convolution (CKConv) that learns to voxelize the features of local points by exploiting both continuous and discrete convolutions. Our continuous convolution uniquely employs a 3D cubic form of kernel weight representation that splits a feature into voxels in embedding space. By consecutively applying<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2107.12655v1">arXiv:2107.12655v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ctcdhjn3lnhlbmdw3cd6s63pfi">fatcat:ctcdhjn3lnhlbmdw3cd6s63pfi</a> </span>
more »... 3D convolutions on the voxelized features in a spatial manner, preceding continuous convolution is forced to learn spatial feature mapping, i.e., feature voxelization. In this way, geometric information can be detailed by encoding with subdivided features, and our 3D convolutions on these fixed structured data do not suffer from discretization artifacts thanks to voxelization in embedding space. Furthermore, we propose a spatial attention module, Local Set Attention (LSA), to provide comprehensive structure awareness within the local point set and hence produce representative features. By learning feature voxelization with LSA, CKConv can extract enriched features for effective point cloud analysis. We show that CKConv has great applicability to point cloud processing tasks including object classification, object part segmentation, and scene semantic segmentation with state-of-the-art results.
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