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
Adaptive Channel Encoding Transformer for Point Cloud Analysis—0
Bridging the Gap: Point Clouds for Merging Neurons in Connectomics—0
CT-block: a novel local and global features extractor for point cloud—0
Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point ModelingCode1
Imperceptible Transfer Attack and Defense on 3D Point Cloud Classification—0
DeltaConv: Anisotropic Operators for Geometric Deep Learning on Point CloudsCode1
Two Heads are Better than One: Geometric-Latent Attention for Point Cloud Classification and SegmentationCode0
RefRec: Pseudo-labels Refinement via Shape Reconstruction for Unsupervised 3D Domain AdaptationCode0
ASSANet: An Anisotropic Separable Set Abstraction for Efficient Point Cloud Representation LearningCode1
PatchAugment: Local Neighborhood Augmentation in Point Cloud ClassificationCode0
Point Cloud Augmentation with Weighted Local TransformationsCode1
Haar Wavelet Feature Compression for Quantized Graph Convolutional Networks—0
Adversarial Attack by Limited Point Cloud Surface Modifications—0
Automated Mobile Attention KPConv Networks via A Wide & Deep Predictor—0
3D Point Cloud Completion with Geometric-Aware Adversarial Augmentation—0
PointManifoldCut: Point-wise Augmentation in the Manifold for Point CloudsCode0
Self-supervised Point Cloud Representation Learning via Separating Mixed ShapesCode1
Geometry-Aware Self-Training for Unsupervised Domain Adaptationon Object Point CloudsCode1
Dual-Neighborhood Deep Fusion Network for Point Cloud Analysis—0
Adaptive Graph Convolution for Point Cloud AnalysisCode0
DRINet: A Dual-Representation Iterative Learning Network for Point Cloud Segmentation—0
Point Discriminative Learning for Data-efficient 3D Point Cloud Analysis—0
Surrogate Model-Based Explainability Methods for Point Cloud NNsCode1
Rotation Transformation Network: Learning View-Invariant Point Cloud for Classification and SegmentationCode0
Training or Architecture? How to Incorporate Invariance in Neural Networks—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