LIDAR Semantic Segmentation
53 papers with code • 4 benchmarks • 7 datasets
Benchmarks
These leaderboards are used to track progress in LIDAR Semantic Segmentation
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Libraries
Use these libraries to find LIDAR Semantic Segmentation models and implementationsDatasets
nuScenes
SemanticKITTI
ScribbleKITTI
Paris-Lille-3D
A Curb Dataset
nuScenes (Cross-City UDA)
ULS labeled data
Most implemented papers
SSD: Single Shot MultiBox Detector
Experimental results on the PASCAL VOC, MS COCO, and ILSVRC datasets confirm that SSD has comparable accuracy to methods that utilize an additional object proposal step and is much faster, while providing a unified framework for both training and inference.
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.
Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution
mit-han-lab/spvnas
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ECCV 2020
Self-driving cars need to understand 3D scenes efficiently and accurately in order to drive safely.
PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentation
edwardzhou130/PolarSeg
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CVPR 2020
The need for fine-grained perception in autonomous driving systems has resulted in recently increased research on online semantic segmentation of single-scan LiDAR.
LENet: Lightweight And Efficient LiDAR Semantic Segmentation Using Multi-Scale Convolution Attention
fengluodb/lenet
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LiDAR-based semantic segmentation is critical in the fields of robotics and autonomous driving as it provides a comprehensive understanding of the scene.
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.
Scribble-Supervised LiDAR Semantic Segmentation
ouenal/scribblekitti
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CVPR 2022
Densely annotating LiDAR point clouds remains too expensive and time-consuming to keep up with the ever growing volume of data.
CPGNet: Cascade Point-Grid Fusion Network for Real-Time LiDAR Semantic Segmentation
LiDAR semantic segmentation essential for advanced autonomous driving is required to be accurate, fast, and easy-deployed on mobile platforms.
Point Transformer V3: Simpler, Faster, Stronger
This paper is not motivated to seek innovation within the attention mechanism.
