SOTAVerified

Point Cloud Classification

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

Papers

Showing 141150 of 265 papers

TitleStatusHype
Rethinking Attention Module Design for Point Cloud Analysis0
Density-Aware Convolutional Networks with Context Encoding for Airborne LiDAR Point Cloud Classification0
DG-MVP: 3D Domain Generalization via Multiple Views of Point Clouds for Classification0
Rethinking Gradient-based Adversarial Attacks on Point Cloud Classification0
DRINet: A Dual-Representation Iterative Learning Network for Point Cloud Segmentation0
Dual-Neighborhood Deep Fusion Network for Point Cloud Analysis0
Dual Transformer for Point Cloud Analysis0
Unsupervised Feedforward Feature (UFF) Learning for Point Cloud Classification and Segmentation0
Equivariance with Learned Canonicalization Functions0
Evaluating Machine Learning Models with NERO: Non-Equivariance Revealed on Orbits0
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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