SOTAVerified

3D Point Cloud Classification

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

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
Show:102550
← PrevPage 7 of 9Next →
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
6AsymDSD-B* (no voting)Overall Accuracy94.7—Unverified
7ULIP + PointMLPOverall Accuracy94.7—Unverified
8ReConOverall Accuracy94.7—Unverified
9RepSurf-UOverall 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
9DGCNN+PointCutMix-RError Rate0.17—Unverified
10PCT+RSMixError Rate0.17—Unverified