Papers › Multi Projection Fusion for Real-time Semantic Segmentation of 3D LiDAR Point Clouds
Multi Projection Fusion for Real-time Semantic Segmentation of 3D LiDAR Point Clouds
Yara Ali Alnaggar, Mohamed Afifi, Karim Amer, Mohamed ElHelw
Semantic segmentation of 3D point cloud data is essential for enhanced high-level perception in autonomous platforms. Furthermore, given the increasing deployment of LiDAR sensors onboard of cars and drones, a special emphasis is also placed on non-computationally intensive algorithms that operate on mobile GPUs. Previous efficient state-of-the-art methods relied on 2D spherical projection of point clouds as input for 2D fully convolutional neural networks to balance the accuracy-speed trade-off. This paper introduces a novel approach for 3D point cloud semantic segmentation that exploits multiple projections of the point cloud to mitigate the loss of information inherent in single projection methods. Our Multi-Projection Fusion (MPF) framework analyzes spherical and bird's-eye view projections using two separate highly-efficient 2D fully convolutional models then combines the segmentation results of both views. The proposed framework is validated on the SemanticKITTI dataset where it achieved a mIoU of 55.5 which is higher than state-of-the-art projection-based methods RangeNet++ and PolarNet while being 1.6x faster than the former and 3.1x faster than the latter.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| 3D Semantic Segmentation | SemanticKITTI | MPF | test mIoU | 55.5% | #28 of 45 | Archive leaderboard | report |
| LIDAR Semantic Segmentation | SemanticKITTI | MPF | mIOU | 55.5% | #4 of 4 | Archive leaderboard | report |
| Real-Time 3D Semantic Segmentation | SemanticKITTI | MPF | Speed (FPS) | 20.6 | #4 of 4 | Archive leaderboard | report |
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