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Efficient 3D Deep Learning

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# Efficient 3D Deep Learning

This repo contains our recent advances in efficient 3D point cloud understanding.

## News

**[2020-09]** We release baseline training code for SPVCNNs and MinkowskiNets in [SPVNAS](https://github.com/mit-han-lab/spvnas) repo, please have a look!

**[2020-08]** Please check out our ECCV 2020 tutorial on [AutoML for Efficient 3D Deep Learning](https://www.youtube.com/watch?v=zzJR07LMXxs), which summarizes the methods released in this codebase. We also made the hands-on tutorial available in colab.

**[2020-07]** Our [paper](https://arxiv.org/abs/2007.16100) Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution is accepted to ECCV 2020.

**[2020-03]** Our work [PVCNN](https://arxiv.org/abs/1907.03739) is deployed on MIT Driverless racing cars, please check of this [video](https://www.youtube.com/watch?v=WW9paispAW0).

**[2019-12]** We give the spotlight talk of [PVCNN](https://arxiv.org/abs/1907.03739) at NeurIPS 2019.

## Projects

- [PVCNN](https://github.com/mit-han-lab/pvcnn): Point-Voxel CNN for Efficient 3D Deep Learning
- [SPVNAS](https://github.com/mit-han-lab/spvnas): Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution
- [TorchSparse](https://github.com/mit-han-lab/torchsparse): High-Performance Neural Network Library for Point Cloud Processing

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