LIDAR Semantic Segmentation
12 papers with code • 3 benchmarks • 4 datasets
Benchmarks
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nuScenes
SemanticKITTI
Paris-Lille-3D
A Curb Dataset
Greatest papers with code
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.
Ranked #2 on
3D Semantic Segmentation
on SensatUrban
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.
Ranked #18 on
3D Semantic Segmentation
on SemanticKITTI
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.
Ranked #1 on
Semantic Segmentation
on ParisLille3D
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.
Ranked #2 on
3D Semantic Segmentation
on SemanticKITTI
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.
Ranked #6 on
LIDAR Semantic Segmentation
on nuScenes
ConvPoint: Continuous Convolutions for Point Cloud Processing
Point clouds are unstructured and unordered data, as opposed to images.
Ranked #3 on
LIDAR Semantic Segmentation
on Paris-Lille-3D
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.
Ranked #1 on
Real-Time 3D Semantic Segmentation
on SemanticKITTI
FG-Net: Fast Large-Scale LiDAR Point Clouds Understanding Network Leveraging Correlated Feature Mining and Geometric-Aware Modelling
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.
Ranked #1 on
3D Part Segmentation
on ShapeNet-Part
KPRNet: Improving projection-based LiDAR semantic segmentation
DeyvidKochanov-TomTom/kprnet
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Semantic segmentation is an important component in the perception systems of autonomous vehicles.
Ranked #5 on
3D Semantic Segmentation
on SemanticKITTI
FIDNet: LiDAR Point Cloud Semantic Segmentation with Fully Interpolation Decoding
placeforyiming/iros21-fidnet-semantickitti
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In this paper, we propose a new projection-based LiDAR semantic segmentation pipeline that consists of a novel network structure and an efficient post-processing step.
