SOTAVerified

Point Cloud Classification

Point Cloud Classification is a task involving the classification of unordered 3D point sets (point clouds).

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1–25 of 265 papers

TitleStatusHype
FRACTAL: An Ultra-Large-Scale Aerial Lidar Dataset for 3D Semantic Segmentation of Diverse LandscapesCode3
Parameter-Efficient Fine-Tuning in Spectral Domain for Point Cloud LearningCode3
Point Transformer V2: Grouped Vector Attention and Partition-based PoolingCode2
Frozen Transformers in Language Models Are Effective Visual Encoder LayersCode2
Curvature Diversity-Driven Deformation and Domain Alignment for Point CloudCode2
Beyond Self-attention: External Attention using Two Linear Layers for Visual TasksCode2
Geometry-Aware Self-Training for Unsupervised Domain Adaptationon Object Point CloudsCode1
Generative PointNet: Deep Energy-Based Learning on Unordered Point Sets for 3D Generation, Reconstruction and ClassificationCode1
Geometry-Aware Self-Training for Unsupervised Domain Adaptation on Object Point CloudsCode1
Dynamic Graph CNN for Learning on Point CloudsCode1
3DCTN: 3D Convolution-Transformer Network for Point Cloud ClassificationCode1
DeltaConv: Anisotropic Operators for Geometric Deep Learning on Point CloudsCode1
A Closer Look at Few-Shot 3D Point Cloud ClassificationCode1
APSNet: Attention Based Point Cloud SamplingCode1
Dynamic Local Feature Aggregation for Learning on Point CloudsCode1
CLIP2Point: Transfer CLIP to Point Cloud Classification with Image-Depth Pre-trainingCode1
Beyond single receptive field: A receptive field fusion-and-stratification network for airborne laser scanning point cloud classificationCode1
Collect-and-Distribute Transformer for 3D Point Cloud AnalysisCode1
Deep Declarative Networks: A New HopeCode1
Deep SetsCode1
Dense-Resolution Network for Point Cloud Classification and SegmentationCode1
Differentiable Euler Characteristic Transforms for Shape ClassificationCode1
Benchmarking and Analyzing Point Cloud Classification under CorruptionsCode1
APP-Net: Auxiliary-point-based Push and Pull Operations for Efficient Point Cloud ClassificationCode1
ASSANet: An Anisotropic Separable Set Abstraction for Efficient Point Cloud Representation LearningCode1
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All datasetsPointCloud-CISPRS

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PointNetmean Corruption Error (mCE)1.42—Unverified
2WOLFMix (PointNet)mean Corruption Error (mCE)1.18—Unverified
3PointNetmean Corruption Error (mCE)1.18—Unverified
4RSCNNmean Corruption Error (mCE)1.13—Unverified
5PAConvmean Corruption Error (mCE)1.1—Unverified
6SimpleViewmean Corruption Error (mCE)1.05—Unverified
7OcCo-DGCNNmean Corruption Error (mCE)1.05—Unverified
8PointMixUp (PointNet++)mean Corruption Error (mCE)1.03—Unverified
9DGCNNmean Corruption Error (mCE)1—Unverified
10OcCo-DGCNNmean Corruption Error (mCE)0.98—Unverified
#ModelMetricClaimedVerifiedStatus
1OursAverage F182.8—Unverified