Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution

@article{Tang2020SearchingE3,
  title={Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution},
  author={Haotian Tang and Zhijian Liu and Shengyu Zhao and Yujun Lin and Ji Lin and Hanrui Wang and Song Han},
  journal={ArXiv},
  year={2020},
  volume={abs/2007.16100}
}
Self-driving cars need to understand 3D scenes efficiently and accurately in order to drive safely. Given the limited hardware resources, existing 3D perception models are not able to recognize small instances (e.g., pedestrians, cyclists) very well due to the low-resolution voxelization and aggressive downsampling. To this end, we propose Sparse Point-Voxel Convolution (SPVConv), a lightweight 3D module that equips the vanilla Sparse Convolution with the high-resolution point-based branch… Expand
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