FG-Net: Fast Large-Scale LiDAR Point Clouds Understanding Network Leveraging Correlated Feature Mining and Geometric-Aware Modelling

TL;DR

FG-Net is a deep learning framework for large-scale LiDAR point cloud understanding that achieves high accuracy and real-time performance without voxelization, utilizing novel filtering, feature mining, and geometric-aware modeling techniques.

Contribution

The paper introduces FG-Net, a novel deep learning architecture with correlated feature mining and geometric-aware modeling for efficient large-scale point cloud understanding.

Findings

Outperforms state-of-the-art in accuracy and efficiency

Achieves real-time performance on a single GPU

Demonstrates strong generalization through transfer learning

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.