Submanifold Sparse Convolutional Networks
See original GitHub issuehttps://arxiv.org/abs/1706.01307 https://github.com/facebookresearch/SparseConvNet
New paper from Facebook for dealing with sparse, volumetric data. Comes with some nice PyTorch code too! This could be a very useful paper for analyzing binding pockets featurized volumetrically (grid_featurizer
does this already).
PRs adding submanifold sparse models to contrib/
welcome!
Issue Analytics
- State:
- Created 6 years ago
- Comments:8 (4 by maintainers)
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Submanifold Sparse Convolutional Networks - arXiv
Abstract: Convolutional network are the de-facto standard for analysing spatio-temporal data such as images, videos, 3D shapes, etc.
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We introduce new sparse convolutional operations that are designed to pro- cess spatially-sparse data more efficiently, and use them to develop spatially-sparse ......
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The authors of “Submanifold Sparse Convolutional Networks” calls this phenomenon the submanifold expansion problem. Submanifolds refer to ...
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Submanifold Convolution (SC) is a spatially sparse convolution operation used for tasks with sparse data like semantic segmentation of 3D point clouds.
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Top GitHub Comments
Sounds good! PRs adding submanifold sparse networks to contrib/ would be very welcome.
I’m going to close this issue since it looks like a very involved port, especially since we’ve decided to go all-in on TensorFlow instead of PyTorch for the time being.