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 211–220 of 265 papers

TitleStatusHype
Efficient and Stable Graph Scattering Transforms via Pruning—0
Efficient Converted Spiking Neural Network for 3D and 2D Classification—0
Efficient Point Cloud Classification via Offline Distillation Framework and Negative-Weight Self-Distillation Technique—0
Eidos: Efficient, Imperceptible Adversarial 3D Point Clouds—0
EllipsoidNet: Ellipsoid Representation for Point Cloud Classification and Segmentation—0
Empowering Knowledge Distillation via Open Set Recognition for Robust 3D Point Cloud Classification—0
Enhancing Local Feature Learning Using Diffusion for 3D Point Cloud Understanding—0
Enhancing Sampling Protocol for Point Cloud Classification Against Corruptions—0
Equivariance with Learned Canonicalization Functions—0
Evaluating Machine Learning Models with NERO: Non-Equivariance Revealed on Orbits—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