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 251–260 of 265 papers

TitleStatusHype
Local region-learning modules for point cloud classification—0
Low-Density 3D 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
Understanding Key Point Cloud Features for Development Three-dimensional Adversarial Attacks—0
RS2AD: End-to-End Autonomous Driving Data Generation from Roadside Sensor Observations—0
RW-Net: Enhancing Few-Shot Point Cloud Classification with a Wavelet Transform Projection-based Network—0
Multi-scale Geometry-aware Transformer for 3D Point Cloud Classification—0
Multi-view Vision-Prompt Fusion Network: Can 2D Pre-trained Model Boost 3D Point Cloud Data-scarce Learning?—0
Neural Attentive Circuits—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