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 161–170 of 265 papers

TitleStatusHype
Local Neighborhood Features for 3D ClassificationCode0
PointCA: Evaluating the Robustness of 3D Point Cloud Completion Models Against Adversarial Examples—0
Equivariance with Learned Canonicalization Functions—0
Point-Voxel Adaptive Feature Abstraction for Robust Point Cloud ClassificationCode0
Text2Model: Text-based Model Induction for Zero-shot Image Classification—0
Understanding Key Point Cloud Features for Development Three-dimensional Adversarial Attacks—0
Self-Distillation for Unsupervised 3D Domain Adaptation—0
Neural Attentive Circuits—0
Automated Mobile Attention KPConv Networks via a Wide and Deep Predictor—0
A Simple Strategy to Provable Invariance via Orbit Mapping—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