Bilevel Convolutional Neural Networks for 3D Semantic Segmentation Using Large-scale LiDAR Point Clouds in Complex Environments(道路点云场景双层卷积语义分割)

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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.

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With the rapid development of the reality capture, such as laser scanning and oblique photogrammetry, point cloud has become the third important data source following vector maps and imagery, and also plays an increasingly important role in scientific research and engineering in the fields of earth science, spatial cognition, and smart city, and so on. However, how to acquire valid and accurate three-dimensional geospatial information from point clouds has become the scientific frontier and the urgent demand in the field of surveying and mapping as well as the geoscience applications. To address the challenges mentioned above, point cloud intelligence came into being. This paper summarizes the state-of-the art of point cloud intelligence in acquisition equipment, the intelligent processing, scientific research and the major engineering applications, focusing on its three important areas: the theoretical methods, the key techniques of intelligent processing and the major engineering applications. Finally, the promising development tendency of the point cloud intelligence is summarized.