Papers › FG-Net: Fast Large-Scale LiDAR Point Clouds Understanding Network Leveraging...
FG-Net: Fast Large-Scale LiDAR Point Clouds Understanding Network Leveraging Correlated Feature Mining and Geometric-Aware Modelling
Kangcheng Liu, Zhi Gao, Feng Lin, Ben M. Chen
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.
In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
1 archive task tag without a task page not shown.
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| 3D Part Segmentation | ShapeNet-Part | Feature Geometric Net (FG-Net) | Class Average IoU | 87.7 | #17 of 67 | Archive leaderboard | report |
| 3D Part Segmentation | ShapeNet-Part | Feature Geometric Net (FG-Net) | Instance Average IoU | 86.6 | #17 of 67 | Archive leaderboard | report |
| 3D Point Cloud Classification | ModelNet40 | Feature Geometric Net (FG-Net) | Mean Accuracy | 91.1 | #40 of 111 | Archive leaderboard | report |
| 3D Point Cloud Classification | ModelNet40 | Feature Geometric Net (FG-Net) | Overall Accuracy | 93.8 | #40 of 111 | Archive leaderboard | report |
| 3D Semantic Segmentation | PartNet | FG-Net | mIOU | 58.2 | #3 of 6 | Archive leaderboard | report |
| 3D Semantic Segmentation | SemanticKITTI | FG-Net | test mIoU | 53.8% | #31 of 45 | Archive leaderboard | report |
| LIDAR Semantic Segmentation | Paris-Lille-3D | Feature Geometric Net (FG Net) | mIOU | 0.819 | #2 of 9 | Archive leaderboard | report |
| Semantic Segmentation | S3DIS | Feature Geometric Net (FG-Net) | Mean IoU | 70.8 | #21 of 54 | Archive leaderboard | report |
| Semantic Segmentation | S3DIS | Feature Geometric Net (FG-Net) | Number of params | N/A | #21 of 54 | Archive leaderboard | report |
| Semantic Segmentation | S3DIS | Feature Geometric Net (FG-Net) | mAcc | 82.9 | #21 of 54 | Archive leaderboard | report |
| Semantic Segmentation | S3DIS | Feature Geometric Net (FG-Net) | oAcc | 88.2 | #21 of 54 | Archive leaderboard | report |
| Semantic Segmentation | ScanNet | FG-Net | test mIoU | 69.0 | #34 of 45 | Archive leaderboard | report |
| Semantic Segmentation | Semantic3D | Feature Geometric Net | mIoU | 78.2% | #1 of 17 | Archive leaderboard | report |
| Semantic Segmentation | Semantic3D | Feature Geometric Net | oAcc | 93.6 | #1 of 17 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections