This is the implementation of LargeKernel3D (CVPR 2023). Large kernels are important but expensive in 3D CNNs. We propose spatial-wise partition to conv enable 3D large kernels. High performance on 3D semantic segmentation & object detection. For more details, please refer to:

LargeKernel3D: Scaling up Kernels in 3D Sparse CNNs [Paper]
Yukang Chen, Jianhui Liu, Xiangyu Zhang, Xiaojuan Qi, Jiaya Jia

If you find this project useful in your research, please consider citing:

@inproceedings{chen2023largekernel3d,
  title={LargeKernel3D: Scaling up Kernels in 3D Sparse CNNs},
  author={Yukang Chen and Jianhui Liu and Xiangyu Zhang and Xiaojuan Qi and Jiaya Jia},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year={2023}
}

• This work is built upon FocalsConv for object detection.
• This work is built upon Stratified-Transformer for semantic segmentation.

• VoxelNeXt (CVPR 2023) [Paper] [Code] Fully Sparse VoxelNet for 3D Object Detection and Tracking.
• Focal Sparse Conv (CVPR 2022 Oral) [Paper] [Code] Dynamic sparse convolution for high performance.
• Spatial Pruned Conv (NeurIPS 2022) [Paper] [Code] 50% FLOPs saving for efficient 3D object detection.
• LargeKernel3D (CVPR 2023) [Paper] [Code] Large-kernel 3D sparse CNN backbone.
• SphereFormer (CVPR 2023) [Paper] [Code] Spherical window 3D transformer backbone.
• spconv-plus A library where we combine our works into spconv.
• SparseTransformer A library that includes high-efficiency transformer implementations for sparse point cloud or voxel data.

This project is released under the Apache 2.0 license.