SOTAVerified

Point Cloud Classification

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

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 151–175 of 265 papers

TitleStatusHype
Interpreting Hidden Semantics in the Intermediate Layers of 3D Point Cloud Classification Neural Network—0
PointCert: Point Cloud Classification with Deterministic Certified Robustness Guarantees—0
CLR-GAM: Contrastive Point Cloud Learning with Guided Augmentation and Feature Mapping—0
S3I-PointHop: SO(3)-Invariant PointHop for 3D Point Cloud Classification—0
PointWavelet: Learning in Spectral Domain for 3D Point Cloud Analysis—0
Learnable Skeleton-Aware 3D Point Cloud Sampling—0
Improved Training for 3D Point Cloud ClassificationCode0
Efficient Converted Spiking Neural Network for 3D and 2D Classification—0
Computation and Data Efficient Backdoor Attacks—0
CAP: Robust Point Cloud Classification via Semantic and Structural Modeling—0
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
3DGTN: 3D Dual-Attention GLocal Transformer Network for Point Cloud Classification and Segmentation—0
SimpleView++: Neighborhood Views for Point Cloud ClassificationCode0
Quantifying the Knowledge in a DNN to Explain Knowledge Distillation for Classification—0
Boosting Point-BERT by Multi-choice TokensCode0
Explaining Deep Neural Networks for Point Clouds using Gradient-based Visualisations—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