论文标题
FG-NET：快速的大规模激光雷达点云了解网络利用相关特征挖掘和几何感知建模
FG-Net: Fast Large-Scale LiDAR Point Clouds Understanding Network Leveraging Correlated Feature Mining and Geometric-Aware Modelling
论文作者
论文摘要
这项工作介绍了FG-NET，这是一个通用的深度学习框架，用于大规模的点云理解而无需Voxelization，它使用单个NVIDIA GTX 1080 GPU实现了准确的实时性能。首先，一种新颖的噪声和离群过滤方法旨在促进随后的高级任务。为了有效理解目的，我们提出了一个深层的卷积神经网络利用相关的特征挖掘和基于可变形的几何学意识建模，可以完全利用局部特征关系和几何模式。对于效率问题，我们提出了一个反密度采样操作和基于特征金字塔的剩余学习策略，以分别节省计算成本和内存消耗。关于现实世界中挑战数据集的广泛实验表明，我们的方法在准确性和效率方面表现优于最先进的方法。此外，还进行了弱监督的转移学习，以证明我们方法的概括能力。
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.
