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 191–200 of 265 papers

TitleStatusHype
RefRec: Pseudo-labels Refinement via Shape Reconstruction for Unsupervised 3D Domain AdaptationCode0
PatchAugment: Local Neighborhood Augmentation in Point Cloud ClassificationCode0
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
Dual-Neighborhood Deep Fusion Network for Point Cloud Analysis—0
Adaptive Graph Convolution for Point Cloud AnalysisCode0
DRINet: A Dual-Representation Iterative Learning Network for Point Cloud Segmentation—0
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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