FG-Net: A Fast and Accurate Framework for Large-Scale LiDAR Point Cloud Understanding
提出FG-Net框架，无需体素化即可实时准确理解大规模点云，通过噪声过滤、特征挖掘和变形卷积捕捉几何结构，并用逆密度采样和特征金字塔降低计算成本，在八个基准上超越现有方法。
This work presents FG-Net, a general deep learning framework for large-scale point cloud understanding without voxelizations, which achieves accurate and real-time performance with a single NVIDIA GTX 1080 8G GPU and an i7 CPU. First, a novel noise and outlier filtering method is designed to facilitate the subsequent high-level understanding tasks. For effective understanding purpose, we propose a novel plug-and-play module consisting of correlated feature mining and deformable convolution-based geometric-aware modeling, in which the local feature relationships and point cloud geometric structures can be fully extracted and exploited. For the efficiency issue, we put forward a new composite inverse density sampling (IDS)-based and learning-based operation and a feature pyramid-based residual learning strategy to save the computational cost and memory consumption, respectively. Compared with current methods which are only validated on limited datasets, we have done extensive experiments on eight real-world challenging benchmarks, which demonstrates that our approaches outperform state-of-the-art (SOTA) approaches in terms of accuracy, speed, and memory efficiency. Moreover, weakly supervised transfer learning is also conducted to demonstrate the generalization capacity of our method.