INS-Conv: Incremental Sparse Convolution for Online 3D segmentation

Leyao Liu, Tian Zheng, Yun-Jou Lin, Kai Ni, Lu Fang.
InProceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2022.

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This is the incremental sparse convolution (Ins-Conv) library implemented based on SparseConvNet and Live Semantic 3D Perception for Immersive Augmented Reality. The later describes a more efficient GPU implementation of the original submanifold sparse convolution. Our method supports incremental computing of sparse convolution, including SSC, convolution/deconvolution, BN, IO, and residual structure, etc.



If you find our code useful, please kindly cite our paper:

  title={INS-Conv: Incremental Sparse Convolution for Online 3D Segmentation},
  author={Liu, Leyao and Zheng, Tian and Lin, Yun-Jou and Ni, Kai and Fang, Lu},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},