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3D Point Cloud Classification

Papers

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Showing 151–175 of 202 papers

TitleStatusHype
Empowering Knowledge Distillation via Open Set Recognition for Robust 3D Point Cloud Classification—0
CAP: Robust Point Cloud Classification via Semantic and Structural Modeling—0
Evaluating Machine Learning Models with NERO: Non-Equivariance Revealed on Orbits—0
Exploiting Local Geometry for Feature and Graph Construction for Better 3D Point Cloud Processing with Graph Neural Networks—0
FFF: Fragment-Guided Flexible Fitting for Building Complete Protein Structures—0
Structural Relational Reasoning of Point Clouds—0
Computation and Data Efficient Backdoor Attacks—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
3D-JEPA: A Joint Embedding Predictive Architecture for 3D Self-Supervised Representation Learning—0
Automated Mobile Attention KPConv Networks via A Wide & Deep Predictor—0
Quantifying the Knowledge in a DNN to Explain Knowledge Distillation for Classification—0
A Benchmark Grocery Dataset of Realworld Point Clouds From Single View—0
Point2Sequence: Learning the Shape Representation of 3D Point Clouds with an Attention-based Sequence to Sequence Network—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
Rethinking Gradient-based Adversarial Attacks on Point Cloud Classification—0
Training or Architecture? How to Incorporate Invariance in Neural Networks—0
A comprehensive overview of deep learning techniques for 3D point cloud classification and semantic segmentation—0
A Simple Strategy to Provable Invariance via Orbit Mapping—0
Multi-scale Geometry-aware Transformer for 3D Point Cloud Classification—0
Imperceptible Transfer Attack and Defense on 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
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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