  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.
