This repo contains our recent advances in efficient 3D point cloud understanding.
[2020-09] We release baseline training code for SPVCNNs and MinkowskiNets in SPVNAS repo, please have a look!
[2020-08] Please check out our ECCV 2020 tutorial on AutoML for Efficient 3D Deep Learning, which summarizes the methods released in this codebase. We also made the hands-on tutorial available in colab.
[2020-07] Our paper Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution is accepted to ECCV 2020.
[2020-03] Our work PVCNN is deployed on MIT Driverless racing cars, please check of this video.
[2019-12] We give the spotlight talk of PVCNN at NeurIPS 2019.
- PVCNN: Point-Voxel CNN for Efficient 3D Deep Learning
- SPVNAS: Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution
- TorchSparse: High-Performance Neural Network Library for Point Cloud Processing