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Robust 3D Semantic Segmentation On

评估指标

mean Corruption Error (mCE)

评测结果

各个模型在此基准测试上的表现结果

模型名称
mean Corruption Error (mCE)
Paper TitleRepository
PolarNet118.56%PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentation
SqueezeSegV2 (64x2048)152.45%SqueezeSegV2: Improved Model Structure and Unsupervised Domain Adaptation for Road-Object Segmentation from a LiDAR Point Cloud
KPConv99.54%KPConv: Flexible and Deformable Convolution for Point Clouds
2DPASS106.14%2DPASS: 2D Priors Assisted Semantic Segmentation on LiDAR Point Clouds
MinkUNet-18100.00%4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks
PIDS-1.2x104.13%PIDS: Joint Point Interaction-Dimension Search for 3D Point Cloud-
Cylinder3D (torchsparse)103.13%Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR Segmentation
CPGNet107.34%CPGNet: Cascade Point-Grid Fusion Network for Real-Time LiDAR Semantic Segmentation
PIDS-2.0x101.20%PIDS: Joint Point Interaction-Dimension Search for 3D Point Cloud-
GFNet108.68%GFNet: Geometric Flow Network for 3D Point Cloud Semantic Segmentation
SPVCNN-3499.16%Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution
CENet (64x2048)103.41%CENet: Toward Concise and Efficient LiDAR Semantic Segmentation for Autonomous Driving
RPVNet111.74%RPVNet: A Deep and Efficient Range-Point-Voxel Fusion Network for LiDAR Point Cloud Segmentation-
WaffleIron109.54%Using a Waffle Iron for Automotive Point Cloud Semantic Segmentation
FIDNet (64x2048)113.81%FIDNet: LiDAR Point Cloud Semantic Segmentation with Fully Interpolation Decoding
Cylinder3D (spconv)103.25%Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR Segmentation
MinkUNet-34100.61%4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks
SalsaNext (64x2048)116.14%SalsaNext: Fast, Uncertainty-aware Semantic Segmentation of LiDAR Point Clouds for Autonomous Driving
SqueezeSeg (64x2048)164.87%SqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D LiDAR Point Cloud
RangeNet-21 (64x2048)136.33%RangeNet++: Fast and Accurate LiDAR Semantic Segmentation
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