3D Point Cloud Classification On Modelnet40 C

Metrics

Error Rate

Results

Performance results of various models on this benchmark

		Paper Title
PointNet	0.283	PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
SimpleView	0.271	Revisiting Point Cloud Classification with a Simple and Effective Baseline
RSCNN	0.262	Relation-Shape Convolutional Neural Network for Point Cloud Analysis
DGCNN	0.259	Dynamic Graph CNN for Learning on Point Clouds
PCT	0.255	PCT: Point cloud transformer
PointNet++	0.236	PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
PointNet++/+PointMixup	0.193	PointMixup: Augmentation for Point Clouds
PointNet++/+PointCutMix-R	0.191	PointCutMix: Regularization Strategy for Point Cloud Classification
DGCNN+PointCutMix-R	0.173	PointCutMix: Regularization Strategy for Point Cloud Classification
PCT+RSMix	0.173	Regularization Strategy for Point Cloud via Rigidly Mixed Sample
PCT+PointCutMix-R	0.163	Benchmarking Robustness of 3D Point Cloud Recognition Against Common Corruptions
OmniVec	0.156	OmniVec: Learning robust representations with cross modal sharing
OmniVec2	0.142	OmniVec2 - A Novel Transformer based Network for Large Scale Multimodal and Multitask Learning

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