SOTAVerified

3D Point Cloud Classification

Papers

Showing 125 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
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PointGSTOverall Accuracy95.3Unverified
2Mamba3D + Point-MAEOverall Accuracy95.1Unverified
3ReCon++Overall Accuracy95Unverified
4PointGPTOverall Accuracy94.9Unverified
5point2vecOverall Accuracy94.8Unverified
6AsymDSD-B* (no voting)Overall Accuracy94.7Unverified
7ULIP + PointMLPOverall Accuracy94.7Unverified
8ReConOverall Accuracy94.7Unverified
9RepSurf-UOverall 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
9DGCNN+PointCutMix-RError Rate0.17Unverified
10PCT+RSMixError Rate0.17Unverified