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 61–70 of 265 papers

TitleStatusHype
Test-Time Augmentation for 3D Point Cloud Classification and Segmentation—0
Learning-Based Biharmonic Augmentation for Point Cloud Classification—0
Deep Learning-based 3D Point Cloud Classification: A Systematic Survey and Outlook—0
Deep Learning-based Compressed Domain Multimedia for Man and Machine: A Taxonomy and Application to Point Cloud Classification—0
Frozen Transformers in Language Models Are Effective Visual Encoder LayersCode2
Differentiable Euler Characteristic Transforms for Shape ClassificationCode1
DualMLP: a two-stream fusion model for 3D point cloud classificationCode0
Edge Aware Learning for 3D Point Cloud—0
Robust Point Cloud Processing through Positional EmbeddingCode1
Synergizing Contrastive Learning and Optimal Transport for 3D Point Cloud Domain Adaptation—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