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 101–125 of 265 papers

TitleStatusHype
CloudFort: Enhancing Robustness of 3D Point Cloud Classification Against Backdoor Attacks via Spatial Partitioning and Ensemble Prediction—0
AIQViT: Architecture-Informed Post-Training Quantization for Vision Transformers—0
Few-Data Guided Learning Upon End-to-End Point Cloud Network for 3D Face Recognition—0
Feature Adversarial Distillation for Point Cloud Classification—0
From depth image to semantic scene synthesis through point cloud classification and labeling: Application to assistive systems—0
CLIP-based Point Cloud Classification via Point Cloud to Image Translation—0
FBPT: A Fully Binary Point Transformer—0
CLR-GAM: Contrastive Point Cloud Learning with Guided Augmentation and Feature Mapping—0
FatNet: A Feature-attentive Network for 3D Point Cloud Processing—0
A Hybrid Generative and Discriminative PointNet on Unordered Point Sets—0
FA-KPConv: Introducing Euclidean Symmetries to KPConv via Frame Averaging—0
3D Shape-Based Myocardial Infarction Prediction Using Point Cloud Classification Networks—0
Global Context Aware Convolutions for 3D Point Cloud Understanding—0
GraNet: Global Relation-aware Attentional Network for ALS Point Cloud Classification—0
Exploiting GPT-4 Vision for Zero-shot Point Cloud Understanding—0
GTNet: Graph Transformer Network for 3D Point Cloud Classification and Semantic Segmentation—0
Haar Wavelet Feature Compression for Quantized Graph Convolutional Networks—0
Explaining Deep Neural Networks for Point Clouds using Gradient-based Visualisations—0
Understanding Key Point Cloud Features for Development Three-dimensional Adversarial Attacks—0
Multi-scale Geometry-aware Transformer for 3D Point Cloud Classification—0
Open-Set Semi-Supervised Learning for 3D Point Cloud Understanding—0
Evaluating Machine Learning Models with NERO: Non-Equivariance Revealed on Orbits—0
Classification of Aerial Photogrammetric 3D Point Clouds—0
Interpreting Hidden Semantics in the Intermediate Layers of 3D Point Cloud Classification Neural Network—0
Low-Density 3D Point Cloud Classification—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