Point Cloud Classification On Pointcloud C

Metrics

mean Corruption Error (mCE)

Results

Performance results of various models on this benchmark

		Paper Title
PointNet	1.422	PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
WOLFMix (PointNet)	1.180	Benchmarking and Analyzing Point Cloud Classification under Corruptions
RSCNN	1.130	Relation-Shape Convolutional Neural Network for Point Cloud Analysis
PAConv	1.104	PAConv: Position Adaptive Convolution with Dynamic Kernel Assembling on Point Clouds
SimpleView	1.047	Revisiting Point Cloud Shape Classification with a Simple and Effective Baseline
OcCo-DGCNN	1.047	Unsupervised Point Cloud Pre-Training via Occlusion Completion
PointMixUp (PointNet++)	1.028	PointMixup: Augmentation for Point Clouds
DGCNN	1.000	Dynamic Graph CNN for Learning on Point Clouds
CurveNet	0.927	Walk in the Cloud: Learning Curves for Point Clouds Shape Analysis
PCT	0.925	PCT: Point cloud transformer
GDANet	0.892	Learning Geometry-Disentangled Representation for Complementary Understanding of 3D Object Point Cloud
RPC	0.863	Benchmarking and Analyzing Point Cloud Classification under Corruptions
PointWOLF (DGCNN)	0.814	Point Cloud Augmentation with Weighted Local Transformations
RSMix (DGCNN)	0.745	Regularization Strategy for Point Cloud via Rigidly Mixed Sample
WOLFMix (RPC)	0.601	Benchmarking and Analyzing Point Cloud Classification under Corruptions
WOLFMix (DGCNN)	0.590	Benchmarking and Analyzing Point Cloud Classification under Corruptions
WOLFMix (GDANet)	0.571	Benchmarking and Analyzing Point Cloud Classification under Corruptions
EPiC(GDANet+WOLFMix)	0.530	EPiC: Ensemble of Partial Point Clouds for Robust Classification
EPiC (DGCNN+WOLFMix)	0.529	EPiC: Ensemble of Partial Point Clouds for Robust Classification
EPiC (PCT+WOLFMix)	0.510	EPiC: Ensemble of Partial Point Clouds for Robust Classification

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Point Cloud Classification On Pointcloud C

Metrics

Results