Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Interpolation-Aware Padding for 3D Sparse Convolutional Neural Networks   Sparse voxel-based 3D convolutional neural networks (CNNs) are widely used
for various 3D vision tasks. Sparse voxel-based 3D CNNs create sparse non-empty
voxels from the 3D input and perform 3D convolution operations on them only. We
propose a simple yet effective padding scheme --- interpolation-aware padding
to pad a few empty voxels adjacent to the non-empty voxels and involve them in
the 3D CNN computation so that all neighboring voxels exist when computing
point-wise features via the trilinear interpolation. For fine-grained 3D vision
tasks where point-wise features are essential, like semantic segmentation and
3D detection, our network achieves higher prediction accuracy than the existing
networks using the nearest neighbor interpolation or the normalized trilinear
interpolation with the zero-padding or the octree-padding scheme. Through
extensive comparisons on various 3D segmentation and detection tasks, we
demonstrate the superiority of 3D sparse CNNs with our padding scheme in
conjunction with feature interpolation.

31
The categories of this paper are: ao-sci
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The categories of this paper are: eess
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The categories of this paper are: solv-int
37
The categories of this paper are: supr-con
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The categories of this paper are: cmp-lg
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The categories of this paper are: q-bio
0
The categories of this paper are: hep-ph
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The categories of this paper are: math-ph
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The categories of this paper are: patt-sol
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The categories of this paper are: hep-th
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The categories of this paper are: econ
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The categories of this paper are: mtrl-th
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The categories of this paper are: q-alg
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The categories of this paper are: nucl-th
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The categories of this paper are: atom-ph
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The categories of this paper are: comp-gas