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 171–180 of 265 papers

TitleStatusHype
AIQViT: Architecture-Informed Post-Training Quantization for Vision Transformers—0
Airborne LiDAR Point Cloud Classification with Graph Attention Convolution Neural Network—0
A Learnable Self-supervised Task for Unsupervised Domain Adaptation on Point Clouds—0
A Network Architecture for Point Cloud Classification via Automatic Depth Images Generation—0
Adaptive Hierarchical Down-Sampling for Point Cloud Classification—0
PointWavelet: Learning in Spectral Domain for 3D Point Cloud Analysis—0
A Simple Strategy to Provable Invariance via Orbit Mapping—0
Attentional ShapeContextNet for Point Cloud Recognition—0
Automated Mobile Attention KPConv Networks via A Wide & Deep Predictor—0
Automated Mobile Attention KPConv Networks via a Wide and Deep Predictor—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