SOTAVerified

3D Point Cloud Classification

Papers

Showing 150 of 202 papers

TitleStatusHype
Parameter-Efficient Fine-Tuning in Spectral Domain for Point Cloud LearningCode3
FRACTAL: An Ultra-Large-Scale Aerial Lidar Dataset for 3D Semantic Segmentation of Diverse LandscapesCode3
ShapeLLM: Universal 3D Object Understanding for Embodied InteractionCode3
PointNeXt: Revisiting PointNet++ with Improved Training and Scaling StrategiesCode3
PointCNN: Convolution On X-Transformed PointsCode3
PCP-MAE: Learning to Predict Centers for Point Masked AutoencodersCode2
KPConvX: Modernizing Kernel Point Convolution with Kernel AttentionCode2
Mamba3D: Enhancing Local Features for 3D Point Cloud Analysis via State Space ModelCode2
PointGPT: Auto-regressively Generative Pre-training from Point CloudsCode2
ULIP-2: Towards Scalable Multimodal Pre-training for 3D UnderstandingCode2
Parameter is Not All You Need: Starting from Non-Parametric Networks for 3D Point Cloud AnalysisCode2
Learning 3D Representations from 2D Pre-trained Models via Image-to-Point Masked AutoencodersCode2
ULIP: Learning a Unified Representation of Language, Images, and Point Clouds for 3D UnderstandingCode2
Point Transformer V2: Grouped Vector Attention and Partition-based PoolingCode2
Let Images Give You More:Point Cloud Cross-Modal Training for Shape AnalysisCode2
Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-trainingCode2
Surface Representation for Point CloudsCode2
Masked Autoencoders for Point Cloud Self-supervised LearningCode2
Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP FrameworkCode2
Benchmarking Robustness of 3D Point Cloud Recognition Against Common CorruptionsCode2
DeepGCNs: Making GCNs Go as Deep as CNNsCode2
SMART-PC: Skeletal Model Adaptation for Robust Test-Time Training in Point CloudsCode1
Point-GN: A Non-Parametric Network Using Gaussian Positional Encoding for Point Cloud ClassificationCode1
Test-Time Adaptation in Point Clouds: Leveraging Sampling Variation with Weight AveragingCode1
PointNet with KAN versus PointNet with MLP for 3D Classification and Segmentation of Point SetsCode1
Robust 3D Point Clouds Classification based on Declarative DefendersCode1
Positional Prompt Tuning for Efficient 3D Representation LearningCode1
RISurConv: Rotation Invariant Surface Attention-Augmented Convolutions for 3D Point Cloud Classification and SegmentationCode1
GPSFormer: A Global Perception and Local Structure Fitting-based Transformer for Point Cloud UnderstandingCode1
Point-JEPA: A Joint Embedding Predictive Architecture for Self-Supervised Learning on Point CloudCode1
Hide in Thicket: Generating Imperceptible and Rational Adversarial Perturbations on 3D Point CloudsCode1
ModelNet-O: A Large-Scale Synthetic Dataset for Occlusion-Aware Point Cloud ClassificationCode1
Towards Compact 3D Representations via Point Feature Enhancement Masked AutoencodersCode1
Regress Before Construct: Regress Autoencoder for Point Cloud Self-supervised LearningCode1
Decoupled Local Aggregation for Point Cloud LearningCode1
Beyond First Impressions: Integrating Joint Multi-modal Cues for Comprehensive 3D RepresentationCode1
Take-A-Photo: 3D-to-2D Generative Pre-training of Point Cloud ModelsCode1
Risk-optimized Outlier Removal for Robust 3D Point Cloud ClassificationCode1
ExpPoint-MAE: Better interpretability and performance for self-supervised point cloud transformersCode1
SUG: Single-dataset Unified Generalization for 3D Point Cloud ClassificationCode1
Instance-aware Dynamic Prompt Tuning for Pre-trained Point Cloud ModelsCode1
A Closer Look at Few-Shot 3D Point Cloud ClassificationCode1
Point2Vec for Self-Supervised Representation Learning on Point CloudsCode1
Self-positioning Point-based Transformer for Point Cloud UnderstandingCode1
Point Cloud Classification Using Content-based Transformer via Clustering in Feature SpaceCode1
Attention-based Point Cloud Edge SamplingCode1
Contrast with Reconstruct: Contrastive 3D Representation Learning Guided by Generative PretrainingCode1
Autoencoders as Cross-Modal Teachers: Can Pretrained 2D Image Transformers Help 3D Representation Learning?Code1
TetraSphere: A Neural Descriptor for O(3)-Invariant Point Cloud AnalysisCode1
SageMix: Saliency-Guided Mixup for Point CloudsCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PointGSTOverall Accuracy95.3Unverified
2Mamba3D + Point-MAEOverall Accuracy95.1Unverified
3ReCon++Overall Accuracy95Unverified
4PointGPTOverall Accuracy94.9Unverified
5point2vecOverall Accuracy94.8Unverified
6RepSurf-UOverall Accuracy94.7Unverified
7ReConOverall Accuracy94.7Unverified
8ULIP + PointMLPOverall Accuracy94.7Unverified
9AsymDSD-B* (no voting)Overall Accuracy94.7Unverified
10PointMLP+HyCoReOverall Accuracy94.5Unverified
#ModelMetricClaimedVerifiedStatus
1OmniVec2Overall Accuracy97.2Unverified
2PointGSTOverall Accuracy96.18Unverified
3OmniVecOverall Accuracy96.1Unverified
4GPSFormerOverall Accuracy95.4Unverified
5ReCon++Overall Accuracy95.25Unverified
6AsymDSD-B* (no voting)Overall Accuracy93.72Unverified
7PointGPTOverall Accuracy93.4Unverified
8GPSFormer-eliteOverall Accuracy93.3Unverified
9Mamba3DOverall Accuracy92.64Unverified
10Mamba3D (no voting)Overall Accuracy91.81Unverified
#ModelMetricClaimedVerifiedStatus
1PointNetError Rate0.28Unverified
2SimpleViewError Rate0.27Unverified
3RSCNNError Rate0.26Unverified
4DGCNNError Rate0.26Unverified
5PCTError Rate0.26Unverified
6PointNet++Error Rate0.24Unverified
7PointNet++/+PointMixupError Rate0.19Unverified
8PointNet++/+PointCutMix-RError Rate0.19Unverified
9PCT+RSMixError Rate0.17Unverified
10DGCNN+PointCutMix-RError Rate0.17Unverified