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SOTA
Robust 3D Semantic Segmentation
Robust 3D Semantic Segmentation On
Robust 3D Semantic Segmentation On
Metrics
mean Corruption Error (mCE)
Results
Performance results of various models on this benchmark
Columns
Model Name
mean Corruption Error (mCE)
Paper Title
SqueezeSeg (64x2048)
164.87%
SqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D LiDAR Point Cloud
SqueezeSegV2 (64x2048)
152.45%
SqueezeSegV2: Improved Model Structure and Unsupervised Domain Adaptation for Road-Object Segmentation from a LiDAR Point Cloud
RangeNet-21 (64x2048)
136.33%
RangeNet++: Fast and Accurate LiDAR Semantic Segmentation
RangeNet-53 (64x2048)
130.66%
RangeNet++: Fast and Accurate LiDAR Semantic Segmentation
PolarNet
118.56%
PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentation
SalsaNext (64x2048)
116.14%
SalsaNext: Fast, Uncertainty-aware Semantic Segmentation of LiDAR Point Clouds for Autonomous Driving
FIDNet (64x2048)
113.81%
FIDNet: LiDAR Point Cloud Semantic Segmentation with Fully Interpolation Decoding
RPVNet
111.74%
RPVNet: A Deep and Efficient Range-Point-Voxel Fusion Network for LiDAR Point Cloud Segmentation
WaffleIron
109.54%
Using a Waffle Iron for Automotive Point Cloud Semantic Segmentation
GFNet
108.68%
GFNet: Geometric Flow Network for 3D Point Cloud Semantic Segmentation
CPGNet
107.34%
CPGNet: Cascade Point-Grid Fusion Network for Real-Time LiDAR Semantic Segmentation
2DPASS
106.14%
2DPASS: 2D Priors Assisted Semantic Segmentation on LiDAR Point Clouds
PIDS-1.2x
104.13%
PIDS: Joint Point Interaction-Dimension Search for 3D Point Cloud
CENet (64x2048)
103.41%
CENet: Toward Concise and Efficient LiDAR Semantic Segmentation for Autonomous Driving
Cylinder3D (spconv)
103.25%
Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR Segmentation
Cylinder3D (torchsparse)
103.13%
Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR Segmentation
PIDS-2.0x
101.20%
PIDS: Joint Point Interaction-Dimension Search for 3D Point Cloud
MinkUNet-34
100.61%
4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks
SPVCNN-18
100.30%
Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution
MinkUNet-18
100.00%
4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks
0 of 22 row(s) selected.
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