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 211–220 of 265 papers

TitleStatusHype
On The Adversarial Robustness of 3D Point Cloud Classification—0
Multi-scale Receptive Fields Graph Attention Network for Point Cloud ClassificationCode0
Unsupervised Feedforward Feature (UFF) Learning for Point Cloud Classification and Segmentation—0
LC-NAS: Latency Constrained Neural Architecture Search for Point Cloud Networks—0
PointMixup: Augmentation for Point CloudsCode1
From depth image to semantic scene synthesis through point cloud classification and labeling: Application to assistive systems—0
Global Context Aware Convolutions for 3D Point Cloud Understanding—0
Cloud Transformers: A Universal Approach To Point Cloud Processing Tasks—0
Point Set Voting for Partial Point Cloud AnalysisCode1
Parameter-Efficient Person Re-identification in the 3D SpaceCode1
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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