SOTAVerified

Point Cloud Classification

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

Papers

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Showing 161–170 of 265 papers

TitleStatusHype
Point Cloud Augmentation with Weighted Local TransformationsCode1
Haar Wavelet Feature Compression for Quantized Graph Convolutional Networks—0
Adversarial Attack by Limited Point Cloud Surface Modifications—0
Automated Mobile Attention KPConv Networks via A Wide & Deep Predictor—0
3D Point Cloud Completion with Geometric-Aware Adversarial Augmentation—0
PointManifoldCut: Point-wise Augmentation in the Manifold for Point CloudsCode0
Self-supervised Point Cloud Representation Learning via Separating Mixed ShapesCode1
Geometry-Aware Self-Training for Unsupervised Domain Adaptationon Object Point CloudsCode1
Dual-Neighborhood Deep Fusion Network for Point Cloud Analysis—0
Adaptive Graph Convolution for Point Cloud AnalysisCode0
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All datasetsPointCloud-CISPRS

Benchmark Results

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