SOTAVerified

Point Cloud Classification

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

Papers

Showing 2130 of 265 papers

TitleStatusHype
SUG: Single-dataset Unified Generalization for 3D Point Cloud ClassificationCode1
Exploiting Inductive Bias in Transformer for Point Cloud Classification and SegmentationCode1
A Closer Look at Few-Shot 3D Point Cloud ClassificationCode1
What Makes for Effective Few-shot Point Cloud Classification?Code1
Point Cloud Classification Using Content-based Transformer via Clustering in Feature SpaceCode1
Dynamic Local Feature Aggregation for Learning on Point CloudsCode1
ViewNet: A Novel Projection-Based Backbone With View Pooling for Few-Shot Point Cloud ClassificationCode1
Implicit Convolutional Kernels for Steerable CNNsCode1
TetraSphere: A Neural Descriptor for O(3)-Invariant Point Cloud AnalysisCode1
MATE: Masked Autoencoders are Online 3D Test-Time LearnersCode1
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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