SOTAVerified

3D Point Cloud Classification

Papers

Showing 51–75 of 202 papers

TitleStatusHype
Evaluating Machine Learning Models with NERO: Non-Equivariance Revealed on Orbits—0
GTNet: Graph Transformer Network for 3D Point Cloud Classification and Semantic Segmentation—0
Connecting Multi-modal Contrastive Representations—0
PointGPT: Auto-regressively Generative Pre-training from Point CloudsCode2
SUG: Single-dataset Unified Generalization for 3D Point Cloud ClassificationCode1
ULIP-2: Towards Scalable Multimodal Pre-training for 3D UnderstandingCode2
Multi-view Vision-Prompt Fusion Network: Can 2D Pre-trained Model Boost 3D Point Cloud Data-scarce Learning?—0
Instance-aware Dynamic Prompt Tuning for Pre-trained Point Cloud ModelsCode1
Multi-scale Geometry-aware Transformer for 3D Point Cloud Classification—0
A Closer Look at Few-Shot 3D Point Cloud ClassificationCode1
Self-positioning Point-based Transformer for Point Cloud UnderstandingCode1
Point2Vec for Self-Supervised Representation Learning on Point CloudsCode1
Parameter is Not All You Need: Starting from Non-Parametric Networks for 3D Point Cloud AnalysisCode2
Interpreting Hidden Semantics in the Intermediate Layers of 3D Point Cloud Classification Neural Network—0
Point Cloud Classification Using Content-based Transformer via Clustering in Feature SpaceCode1
Attention-based Point Cloud Edge SamplingCode1
CLR-GAM: Contrastive Point Cloud Learning with Guided Augmentation and Feature Mapping—0
S3I-PointHop: SO(3)-Invariant PointHop for 3D Point Cloud Classification—0
Contrast with Reconstruct: Contrastive 3D Representation Learning Guided by Generative PretrainingCode1
Improved Training for 3D Point Cloud ClassificationCode0
Computation and Data Efficient Backdoor Attacks—0
FFF: Fragment-Guided Flexible Fitting for Building Complete Protein Structures—0
CAP: Robust Point Cloud Classification via Semantic and Structural Modeling—0
Autoencoders as Cross-Modal Teachers: Can Pretrained 2D Image Transformers Help 3D Representation Learning?Code1
Learning 3D Representations from 2D Pre-trained Models via Image-to-Point Masked AutoencodersCode2
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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