SOTAVerified

Point Cloud Classification

Point Cloud Classification is a task involving the classification of unordered 3D point sets (point clouds).

Papers

Showing 5160 of 265 papers

TitleStatusHype
Point Cloud Augmentation with Weighted Local TransformationsCode1
Self-supervised Point Cloud Representation Learning via Separating Mixed ShapesCode1
Geometry-Aware Self-Training for Unsupervised Domain Adaptationon Object Point CloudsCode1
Surrogate Model-Based Explainability Methods for Point Cloud NNsCode1
Revisiting Point Cloud Shape Classification with a Simple and Effective BaselineCode1
Image2Point: 3D Point-Cloud Understanding with 2D Image Pretrained ModelsCode1
Walk in the Cloud: Learning Curves for Point Clouds Shape AnalysisCode1
Zero-Shot Learning on 3D Point Cloud Objects and BeyondCode1
PAConv: Position Adaptive Convolution with Dynamic Kernel Assembling on Point CloudsCode1
Regularization Strategy for Point Cloud via Rigidly Mixed SampleCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PointNetmean Corruption Error (mCE)1.42Unverified
2WOLFMix (PointNet)mean Corruption Error (mCE)1.18Unverified
3PointNetmean Corruption Error (mCE)1.18Unverified
4RSCNNmean Corruption Error (mCE)1.13Unverified
5PAConvmean Corruption Error (mCE)1.1Unverified
6SimpleViewmean Corruption Error (mCE)1.05Unverified
7OcCo-DGCNNmean Corruption Error (mCE)1.05Unverified
8PointMixUp (PointNet++)mean Corruption Error (mCE)1.03Unverified
9DGCNNmean Corruption Error (mCE)1Unverified
10OcCo-DGCNNmean Corruption Error (mCE)0.98Unverified
#ModelMetricClaimedVerifiedStatus
1OursAverage F182.8Unverified