OpenCodePapers

lidar-semantic-segmentation-on-paris-lille-3d

LIDAR Semantic Segmentation
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PaperCodemIOUModelNameReleaseDate
FKAConv: Feature-Kernel Alignment for Point Cloud Convolution✓ Link0.827FKAConv2020-04-09
FG-Net: Fast Large-Scale LiDAR Point Clouds Understanding Network Leveraging Correlated Feature Mining and Geometric-Aware Modelling✓ Link0.819Feature Geometric Net (FG Net)2020-12-17
Exploiting Local Geometry for Feature and Graph Construction for Better 3D Point Cloud Processing with Graph Neural Networks0.785GeomGCNN2021-03-28
ConvPoint: Continuous Convolutions for Point Cloud Processing✓ Link0.759ConvPoint2019-04-04
KPConv: Flexible and Deformable Convolution for Point Clouds✓ Link0.759KPConv deform2019-04-18
CLOUDSPAM: Contrastive Learning On Unlabeled Data for Segmentation and Pre-Training Using Aggregated Point Clouds and MoCo✓ Link0.738CLOUDSPAM2024-10-26
ConvPoint: Continuous Convolutions for Point Cloud Processing✓ Link0.720ConvPoint_Keras2019-04-04
CLOUDSPAM: Contrastive Learning On Unlabeled Data for Segmentation and Pre-Training Using Aggregated Point Clouds and MoCo✓ Link0.638DA-supervised2024-10-26
Paris-Lille-3D: a large and high-quality ground truth urban point cloud dataset for automatic segmentation and classification0.31Paris-Lille-3D2017-11-30
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