LIDAR Semantic Segmentation
30 papers with code • 3 benchmarks • 6 datasets
Benchmarks
These leaderboards are used to track progress in LIDAR Semantic Segmentation
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Datasets
nuScenes
SemanticKITTI
Paris-Lille-3D
ScribbleKITTI
A Curb Dataset
nuScenes (Cross-City UDA)
Most implemented papers
KPConv: Flexible and Deformable Convolution for Point Clouds
HuguesTHOMAS/KPConv
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ICCV 2019
Furthermore, these locations are continuous in space and can be learned by the network.
RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds
QingyongHu/RandLA-Net
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CVPR 2020
We study the problem of efficient semantic segmentation for large-scale 3D point clouds.
Cylinder3D: An Effective 3D Framework for Driving-scene LiDAR Semantic Segmentation
A straightforward solution to tackle the issue of 3D-to-2D projection is to keep the 3D representation and process the points in the 3D space.
Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR Segmentation
xinge008/Cylinder3D
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CVPR 2021
However, we found that in the outdoor point cloud, the improvement obtained in this way is quite limited.
LaserMix for Semi-Supervised LiDAR Semantic Segmentation
Densely annotating LiDAR point clouds is costly, which restrains the scalability of fully-supervised learning methods.
PolarMix: A General Data Augmentation Technique for LiDAR Point Clouds
The first is scene-level swapping which exchanges point cloud sectors of two LiDAR scans that are cut along the azimuth axis.
ConvPoint: Continuous Convolutions for Point Cloud Processing
Point clouds are unstructured and unordered data, as opposed to images.
RangeNet++: Fast and Accurate LiDAR Semantic Segmentation
PRBonn/lidar-bonnetal
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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2019
Perception in autonomous vehicles is often carried out through a suite of different sensing modalities.
3D-MiniNet: Learning a 2D Representation from Point Clouds for Fast and Efficient 3D LIDAR Semantic Segmentation
Fast and efficient semantic segmentation methods are needed to match the strong computational and temporal restrictions of many of these real-world applications.
FKAConv: Feature-Kernel Alignment for Point Cloud Convolution
valeoai/FKAConv
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Recent state-of-the-art methods for point cloud processing are based on the notion of point convolution, for which several approaches have been proposed.
