Robust 3D Semantic Segmentation On

Metriken

mean Corruption Error (mCE)

Ergebnisse

Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

Modellname	mean Corruption Error (mCE)	Paper Title	Repository
PolarNet	118.56%	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	-
KPConv	99.54%	KPConv: Flexible and Deformable Convolution for Point Clouds	-
2DPASS	106.14%	2DPASS: 2D Priors Assisted Semantic Segmentation on LiDAR Point Clouds	-
MinkUNet-18	100.00%	4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks	-
PIDS-1.2x	104.13%	PIDS: Joint Point Interaction-Dimension Search for 3D Point Cloud	-
Cylinder3D (torchsparse)	103.13%	Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR Segmentation	-
CPGNet	107.34%	CPGNet: Cascade Point-Grid Fusion Network for Real-Time LiDAR Semantic Segmentation	-
PIDS-2.0x	101.20%	PIDS: Joint Point Interaction-Dimension Search for 3D Point Cloud	-
GFNet	108.68%	GFNet: Geometric Flow Network for 3D Point Cloud Semantic Segmentation	-
SPVCNN-34	99.16%	Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution	-
CENet (64x2048)	103.41%	CENet: Toward Concise and Efficient LiDAR Semantic Segmentation for Autonomous Driving	-
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	-
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-34	100.61%	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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