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 201–210 of 265 papers

TitleStatusHype
Rethinking Attention Module Design for Point Cloud Analysis—0
Density-Aware Convolutional Networks with Context Encoding for Airborne LiDAR Point Cloud Classification—0
DG-MVP: 3D Domain Generalization via Multiple Views of Point Clouds for Classification—0
Rethinking Gradient-based Adversarial Attacks on Point Cloud Classification—0
DRINet: A Dual-Representation Iterative Learning Network for Point Cloud Segmentation—0
Dual-Neighborhood Deep Fusion Network for Point Cloud Analysis—0
Dual Transformer for Point Cloud Analysis—0
Unsupervised Feedforward Feature (UFF) Learning for Point Cloud Classification and Segmentation—0
A Benchmark Grocery Dataset of Realworld Point Clouds From Single View—0
Edge Aware Learning for 3D Point Cloud—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