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 176–200 of 265 papers

TitleStatusHype
Enhancing Local Feature Learning Using Diffusion for 3D Point Cloud Understanding—0
Unified Fourier-based Kernel and Nonlinearity Design for Equivariant Networks on Homogeneous Spaces—0
AGConv: Adaptive Graph Convolution on 3D Point CloudsCode0
Transformers in 3D Point Clouds: A Survey—0
Topologically Persistent Features-based Object Recognition in Cluttered Indoor Environments—0
Open-Set Semi-Supervised Learning for 3D Point Cloud Understanding—0
DuMLP-Pin: A Dual-MLP-dot-product Permutation-invariant Network for Set Feature ExtractionCode0
Contrastive Embedding Distribution Refinement and Entropy-Aware Attention for 3D Point Cloud ClassificationCode0
Action Keypoint Network for Efficient Video Recognition—0
On Automatic Data Augmentation for 3D Point Cloud ClassificationCode0
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
Imperceptible Transfer Attack and Defense on 3D Point Cloud Classification—0
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
PatchAugment: Local Neighborhood Augmentation in Point Cloud ClassificationCode0
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
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
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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