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• W3205364888 abstract "This work presents a general deep learning framework for large-scale point clouds understanding without voxelizations, called FG-Conv, which achieves an accurate and real-time understanding of point clouds. Through our novel design combining feature level correlation mining and deformable convolutions based geometric aware modeling, the local feature relationships and geometric patterns can be captured. The attention mechanism is also adopted to enhance the global long-range feature correlations. Finally, the feature pyramid residual learning network is proposed to combine patterns at different resolutions in a memory-efficient way. Extensive experiments on real-world challenging datasets demonstrated that our approaches outperform state-of-the-art methods in terms of accuracy and efficiency. Weakly supervised transfer learning demonstrates the generalization capacity of our methods." @default.
• W3205364888 created "2021-10-25" @default.
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• W3205364888 date "2021-05-30" @default.
• W3205364888 modified "2023-10-01" @default.
• W3205364888 title "FG-Conv: Large-Scale LiDAR Point Clouds Understanding Leveraging Feature Correlation Mining and Geometric-Aware Modeling" @default.
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• W3205364888 doi "https://doi.org/10.1109/icra48506.2021.9561496" @default.
• W3205364888 hasPublicationYear "2021" @default.
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