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

arXiv: https://arxiv.org/abs/2012.09439 · HTML: https://mlsanity.com/paper/2012.09439/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Dec 2020.

## Built on by (most cited first, 17 of 17)

- [ConDor: Self-Supervised Canonicalization of 3D Pose for Partial Shapes](https://mlsanity.com/paper/2201.07788/) (2022)
- [A new weakly supervised approach for ALS point cloud semantic segmentation](https://mlsanity.com/paper/2110.01462/) (2021)
- [Efficient Urban-scale Point Clouds Segmentation with BEV Projection](https://mlsanity.com/paper/2109.09074/) (2021)
- [Deep Learning on 3D Semantic Segmentation: A Detailed Review](https://mlsanity.com/paper/2411.02104/) (2024)
- [APP-Net: Auxiliary-point-based Push and Pull Operations for Efficient Point Cloud Classification](https://mlsanity.com/paper/2205.00847/) (2022)
- [Human Vision Based 3D Point Cloud Semantic Segmentation of Large-Scale Outdoor Scene](https://mlsanity.com/paper/2301.12972/) (2023)
- [Semi-Supervised Confidence-Level-based Contrastive Discrimination for Class-Imbalanced Semantic Segmentation](https://mlsanity.com/paper/2211.15066/) (2022)
- [Multi Point-Voxel Convolution (MPVConv) for Deep Learning on Point Clouds](https://mlsanity.com/paper/2107.13152/) (2021)
- [Graph Classification via Discriminative Edge Feature Learning](https://mlsanity.com/paper/2210.02060/) (2022)
- [Learning-Based Defect Recognitions for Autonomous UAV Inspections](https://mlsanity.com/paper/2302.06093/) (2023)
- [PointCrack3D: Crack Detection in Unstructured Environments using a 3D-Point-Cloud-Based Deep Neural Network](https://mlsanity.com/paper/2111.11615/) (2021)
- [PointResNet: Residual Network for 3D Point Cloud Segmentation and Classification](https://mlsanity.com/paper/2211.11040/) (2022)
- [Online Robot Navigation and Manipulation with Distilled Vision-Language Models](https://mlsanity.com/paper/2401.17083/) (2024)
- [Generating 3D Bio-Printable Patches Using Wound Segmentation and Reconstruction to Treat Diabetic Foot Ulcers](https://mlsanity.com/paper/2203.03814/) (2022)
- [A Review and A Robust Framework of Data-Efficient 3D Scene Parsing with Traditional/Learned 3D Descriptors](https://mlsanity.com/paper/2312.01262/) (2023)
- [A Data-efficient Framework for Robotics Large-scale LiDAR Scene Parsing](https://mlsanity.com/paper/2312.02208/) (2023)
- [Adaptive Scaling with Geometric and Visual Continuity of completed 3D objects](https://mlsanity.com/paper/2602.12905/) (2026)

## Abstract (arXiv)

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.