FG-Net: Fast Large-Scale LiDAR Point CloudsUnderstanding Network Leveraging CorrelatedFeature Mining and Geometric-Aware Modelling

• URL: http://arxiv.org/abs/2012.09439v1
• Date: Thu, 17 Dec 2020 08:20:09 GMT
• Title: FG-Net: Fast Large-Scale LiDAR Point CloudsUnderstanding Network Leveraging CorrelatedFeature Mining and Geometric-Aware Modelling
• Authors: Kangcheng Liu, Zhi Gao, Feng Lin, and Ben M. Chen
• Abstract summary: FG-Net is a general deep learning framework for large-scale point clouds understanding without voxelizations. We propose a deep convolutional neural network leveraging correlated feature mining and deformable convolution based geometric-aware modelling. Our approaches outperform state-of-the-art approaches in terms of accuracy and efficiency.
• Score: 15.059508985699575
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: This work presents FG-Net, a general deep learning framework for large-scale point clouds understanding without voxelizations, which achieves accurate and real-time performance with a single NVIDIA GTX 1080 GPU. First, a novel noise and outlier filtering method is designed to facilitate subsequent high-level tasks. For effective understanding purpose, we propose a deep convolutional neural network leveraging correlated feature mining and deformable convolution based geometric-aware modelling, in which the local feature relationships and geometric patterns can be fully exploited. For the efficiency issue, we put forward an inverse density sampling operation and a feature pyramid based residual learning strategy to save the computational cost and memory consumption respectively. Extensive experiments on real-world challenging datasets demonstrated that our approaches outperform state-of-the-art approaches in terms of accuracy and efficiency. Moreover, weakly supervised transfer learning is also conducted to demonstrate the generalization capacity of our method.

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