SOTAVerified

Point Cloud Classification

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

Papers

Showing 1120 of 265 papers

TitleStatusHype
PointNet with KAN versus PointNet with MLP for 3D Classification and Segmentation of Point SetsCode1
Robust 3D Point Clouds Classification based on Declarative DefendersCode1
Positional Prompt Tuning for Efficient 3D Representation LearningCode1
RISurConv: Rotation Invariant Surface Attention-Augmented Convolutions for 3D Point Cloud Classification and SegmentationCode1
Hide in Thicket: Generating Imperceptible and Rational Adversarial Perturbations on 3D Point CloudsCode1
ModelNet-O: A Large-Scale Synthetic Dataset for Occlusion-Aware Point Cloud ClassificationCode1
Differentiable Euler Characteristic Transforms for Shape ClassificationCode1
Robust Point Cloud Processing through Positional EmbeddingCode1
Risk-optimized Outlier Removal for Robust 3D Point Cloud ClassificationCode1
Collect-and-Distribute Transformer for 3D Point Cloud AnalysisCode1
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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