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 101–150 of 265 papers

TitleStatusHype
3D Point Cloud Completion with Geometric-Aware Adversarial Augmentation—0
3D Shape-Based Myocardial Infarction Prediction Using Point Cloud Classification Networks—0
A Benchmark Grocery Dataset of Realworld Point Clouds From Single View—0
PointShuffleNet: Learning Non-Euclidean Features with Homotopy Equivalence and Mutual Information—0
A comprehensive overview of deep learning techniques for 3D point cloud classification and semantic segmentation—0
Action Keypoint Network for Efficient Video Recognition—0
Adaptive Channel Encoding Transformer for Point Cloud Analysis—0
Adaptive Point Transformer—0
Adversarial Attack by Limited Point Cloud Surface Modifications—0
Adversarial Attacks and Defenses on 3D Point Cloud Classification: A Survey—0
A Hybrid Generative and Discriminative PointNet on Unordered Point Sets—0
AIQViT: Architecture-Informed Post-Training Quantization for Vision Transformers—0
Airborne LiDAR Point Cloud Classification with Graph Attention Convolution Neural Network—0
A Learnable Self-supervised Task for Unsupervised Domain Adaptation on Point Clouds—0
A Network Architecture for Point Cloud Classification via Automatic Depth Images Generation—0
Adaptive Hierarchical Down-Sampling for Point Cloud Classification—0
PointWavelet: Learning in Spectral Domain for 3D Point Cloud Analysis—0
A Simple Strategy to Provable Invariance via Orbit Mapping—0
Attentional ShapeContextNet for Point Cloud Recognition—0
Automated Mobile Attention KPConv Networks via A Wide & Deep Predictor—0
Automated Mobile Attention KPConv Networks via a Wide and Deep Predictor—0
Synergizing Contrastive Learning and Optimal Transport for 3D Point Cloud Domain Adaptation—0
Classification of Single-View Object Point Clouds—0
Temporal Reversed Training for Spiking Neural Networks with Generalized Spatio-Temporal Representation—0
Bridging Domain Gap of Point Cloud Representations via Self-Supervised Geometric Augmentation—0
Bridging the Gap: Point Clouds for Merging Neurons in Connectomics—0
CAP: Robust Point Cloud Classification via Semantic and Structural Modeling—0
CausalPC: Improving the Robustness of Point Cloud Classification by Causal Effect Identification—0
Classification of Aerial Photogrammetric 3D Point Clouds—0
Quantifying the Knowledge in a DNN to Explain Knowledge Distillation for Classification—0
CLIP-based Point Cloud Classification via Point Cloud to Image Translation—0
CloudFort: Enhancing Robustness of 3D Point Cloud Classification Against Backdoor Attacks via Spatial Partitioning and Ensemble Prediction—0
Cloud Transformers: A Universal Approach To Point Cloud Processing Tasks—0
CLR-GAM: Contrastive Point Cloud Learning with Guided Augmentation and Feature Mapping—0
Computation and Data Efficient Backdoor Attacks—0
Connecting Multi-modal Contrastive Representations—0
CT-block: a novel local and global features extractor for point cloud—0
DeeperGCN: Training Deeper GCNs with Generalized Aggregation Functions—0
Deep Learning-based 3D Point Cloud Classification: A Systematic Survey and Outlook—0
Deep Learning-based Compressed Domain Multimedia for Man and Machine: A Taxonomy and Application to Point Cloud Classification—0
Test-Time Augmentation for 3D Point Cloud Classification and Segmentation—0
Rethinking Attention Module Design for Point Cloud Analysis—0
Density-Aware Convolutional Networks with Context Encoding for Airborne LiDAR Point Cloud Classification—0
DG-MVP: 3D Domain Generalization via Multiple Views of Point Clouds for Classification—0
Rethinking Gradient-based Adversarial Attacks on Point Cloud Classification—0
DRINet: A Dual-Representation Iterative Learning Network for Point Cloud Segmentation—0
Dual-Neighborhood Deep Fusion Network for Point Cloud Analysis—0
Dual Transformer for Point Cloud Analysis—0
Unsupervised Feedforward Feature (UFF) Learning for Point Cloud Classification and Segmentation—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