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 111–120 of 265 papers

TitleStatusHype
Exploiting GPT-4 Vision for Zero-shot Point Cloud Understanding—0
Explaining Deep Neural Networks for Point Clouds using Gradient-based Visualisations—0
Global Context Aware Convolutions for 3D Point Cloud Understanding—0
GraNet: Global Relation-aware Attentional Network for ALS Point Cloud Classification—0
PointManifold: Using Manifold Learning for Point Cloud Classification—0
Meta Episodic learning with Dynamic Task Sampling for CLIP-based Point Cloud Classification—0
Haar Wavelet Feature Compression for Quantized Graph Convolutional Networks—0
Evaluating Machine Learning Models with NERO: Non-Equivariance Revealed on Orbits—0
Classification of Aerial Photogrammetric 3D Point Clouds—0
Equivariance with Learned Canonicalization Functions—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