JIANG Tengping, YANG Bisheng, ZHOU Yuzhou, ZHU Runsong, HU Zongtian, DONG Zhen. Bilevel Convolutional Neural Networks for 3D Semantic Segmentation Using Large-scale LiDAR Point Clouds in Complex Environments[J]. Geomatics and Information Science of Wuhan University, 2020, 45(12): 1942-1948. DOI: 10.13203/j.whugis20200081
Citation: JIANG Tengping, YANG Bisheng, ZHOU Yuzhou, ZHU Runsong, HU Zongtian, DONG Zhen. Bilevel Convolutional Neural Networks for 3D Semantic Segmentation Using Large-scale LiDAR Point Clouds in Complex Environments[J]. Geomatics and Information Science of Wuhan University, 2020, 45(12): 1942-1948. DOI: 10.13203/j.whugis20200081

Bilevel Convolutional Neural Networks for 3D Semantic Segmentation Using Large-scale LiDAR Point Clouds in Complex Environments

  • In large-scale road environment, point-based methods require dynamic calculations, and voxel-based methods often lose a lot of information when balancing resolution and performance. To overcome the drawbacks of the above two classical methods, this paper proposes a general network architecture that combines bi-level convolution and dynamic graph edge convolution optimization for multi-object recognition of large-scale road scenes. The framework integrates the convolution operations of two different domains of points and supervoxels to avoid redundant calculations and storage of spatial information in the network. Coupled with the dynamic graph edge convolution optimization, our model enables it to process large-scale point clouds end-to-end at once. Our method was tested and evaluated on different datasets. The experimental results show that our method can achieve higher accuracy in complex road scenes, which is superior to the existing advanced methods.
  • loading

Catalog

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return