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3D Point Cloud Classification

Papers

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Showing 101–150 of 202 papers

TitleStatusHype
Test-Time Adaptation in Point Clouds: Leveraging Sampling Variation with Weight AveragingCode1
TetraSphere: A Neural Descriptor for O(3)-Invariant Point Cloud AnalysisCode1
Towards Compact 3D Representations via Point Feature Enhancement Masked AutoencodersCode1
Transductive Zero-Shot Learning for 3D Point Cloud ClassificationCode1
Walk in the Cloud: Learning Curves for Point Clouds Shape AnalysisCode1
Zero-Shot Learning on 3D Point Cloud Objects and BeyondCode1
Point Discriminative Learning for Data-efficient 3D Point Cloud Analysis—0
Connecting Multi-modal Contrastive Representations—0
Deep Learning-based 3D Point Cloud Classification: A Systematic Survey and Outlook—0
PointMoment:Mixed-Moment-based Self-Supervised Representation Learning for 3D Point Clouds—0
GTNet: Graph Transformer Network for 3D Point Cloud Classification and Semantic Segmentation—0
Training or Architecture? How to Incorporate Invariance in Neural Networks—0
Text2Model: Text-based Model Induction for Zero-shot Image Classification—0
Imperceptible Transfer Attack and Defense on 3D Point Cloud Classification—0
Adversarial Attacks and Defenses on 3D Point Cloud Classification: A Survey—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
Interpolated Convolutional Networks for 3D Point Cloud Understanding—0
Interpreting Hidden Semantics in the Intermediate Layers of 3D Point Cloud Classification Neural Network—0
A Benchmark Grocery Dataset of Realworld Point Clouds From Single View—0
A comprehensive overview of deep learning techniques for 3D point cloud classification and semantic segmentation—0
CLR-GAM: Contrastive Point Cloud Learning with Guided Augmentation and Feature Mapping—0
PointGuard: Provably Robust 3D Point Cloud Classification—0
CloudFort: Enhancing Robustness of 3D Point Cloud Classification Against Backdoor Attacks via Spatial Partitioning and Ensemble Prediction—0
Temporal Reversed Training for Spiking Neural Networks with Generalized Spatio-Temporal Representation—0
Dual-Neighborhood Deep Fusion Network for Point Cloud Analysis—0
S3I-PointHop: SO(3)-Invariant PointHop for 3D Point Cloud Classification—0
MKConv: Multidimensional Feature Representation for Point Cloud Analysis—0
Dual Transformer for Point Cloud Analysis—0
Low-Density 3D Point Cloud Classification—0
A Simple Strategy to Provable Invariance via Orbit Mapping—0
CAP: Robust Point Cloud Classification via Semantic and Structural Modeling—0
PointManifold: Using Manifold Learning for Point Cloud Classification—0
Beyond local patches: Preserving global–local interactions by enhancing self-attention via 3D point cloud tokenization—0
Understanding Key Point Cloud Features for Development Three-dimensional Adversarial Attacks—0
Point2Sequence: Learning the Shape Representation of 3D Point Clouds with an Attention-based Sequence to Sequence Network—0
Multi-scale Geometry-aware Transformer for 3D Point Cloud Classification—0
Multi-view Convolutional Neural Networks for 3D Shape Recognition—0
Multi-view Vision-Prompt Fusion Network: Can 2D Pre-trained Model Boost 3D Point Cloud Data-scarce Learning?—0
3D-JEPA: A Joint Embedding Predictive Architecture for 3D Self-Supervised Representation Learning—0
OmniVec2 - A Novel Transformer based Network for Large Scale Multimodal and Multitask Learning—0
OmniVec: Learning robust representations with cross modal sharing—0
Efficient and Stable Graph Scattering Transforms via Pruning—0
On The Adversarial Robustness of 3D Point Cloud Classification—0
On Adversarial Robustness of 3D Point Cloud Classification under Adaptive Attacks—0
Open-Set Semi-Supervised Learning for 3D Point Cloud Understanding—0
Eidos: Efficient, Imperceptible Adversarial 3D Point Clouds—0
Test-Time Augmentation for 3D Point Cloud Classification and Segmentation—0
Automated Mobile Attention KPConv Networks via a Wide and Deep Predictor—0
Automated Mobile Attention KPConv Networks via A Wide & Deep Predictor—0
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All datasetsModelNet40ScanObjectNNModelNet40-CIntrASydney Urban Objects

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PointGSTOverall Accuracy95.3—Unverified
2Mamba3D + Point-MAEOverall Accuracy95.1—Unverified
3ReCon++Overall Accuracy95—Unverified
4PointGPTOverall Accuracy94.9—Unverified
5point2vecOverall Accuracy94.8—Unverified
6RepSurf-UOverall Accuracy94.7—Unverified
7ReConOverall Accuracy94.7—Unverified
8ULIP + PointMLPOverall Accuracy94.7—Unverified
9AsymDSD-B* (no voting)Overall Accuracy94.7—Unverified
10PointMLP+HyCoReOverall Accuracy94.5—Unverified
#ModelMetricClaimedVerifiedStatus
1OmniVec2Overall Accuracy97.2—Unverified
2PointGSTOverall Accuracy96.18—Unverified
3OmniVecOverall Accuracy96.1—Unverified
4GPSFormerOverall Accuracy95.4—Unverified
5ReCon++Overall Accuracy95.25—Unverified
6AsymDSD-B* (no voting)Overall Accuracy93.72—Unverified
7PointGPTOverall Accuracy93.4—Unverified
8GPSFormer-eliteOverall Accuracy93.3—Unverified
9Mamba3DOverall Accuracy92.64—Unverified
10Mamba3D (no voting)Overall Accuracy91.81—Unverified
#ModelMetricClaimedVerifiedStatus
1PointNetError Rate0.28—Unverified
2SimpleViewError Rate0.27—Unverified
3RSCNNError Rate0.26—Unverified
4DGCNNError Rate0.26—Unverified
5PCTError Rate0.26—Unverified
6PointNet++Error Rate0.24—Unverified
7PointNet++/+PointMixupError Rate0.19—Unverified
8PointNet++/+PointCutMix-RError Rate0.19—Unverified
9PCT+RSMixError Rate0.17—Unverified
10DGCNN+PointCutMix-RError Rate0.17—Unverified