Abstract
6G network enables the rapid connection of autonomous vehicles, the generated internet of vehicles establishes a large-scale point cloud, which requires automatic point cloud analysis to build an intelligent transportation system in terms of the 3D object detection and segmentation. Recently, a great variety of deep convolution networks have been proposed for 3D data analysis, making significant progress in the application of deep learning in 3D computer vision. Inspired by the application of transformer network in 2D computer visual tasks, and in order to increase the expression ability of local fine-grained features, we propose an effective local feature transformer network to learn local feature information and correlations between point clouds. Our network is adaptive to the arrangement of set elements through transformer module, so it is suitable for the feature extraction of local point clouds. In addition, experimental results demonstrate that our LFT-network outperforms the state-of-the-art in 3D model classification tasks on ModelNet40 dataset and segmentation tasks on S3DIS dataset.
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