SOTAVerified

Point Cloud Classification

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

Papers

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Showing 81–90 of 265 papers

TitleStatusHype
Dynamic Local Feature Aggregation for Learning on Point CloudsCode1
A comprehensive overview of deep learning techniques for 3D point cloud classification and semantic segmentation—0
CT-block: a novel local and global features extractor for point cloud—0
A Learnable Self-supervised Task for Unsupervised Domain Adaptation on Point Clouds—0
A Network Architecture for Point Cloud Classification via Automatic Depth Images Generation—0
Geometric Graph Filters and Neural Networks: Limit Properties and Discriminability Trade-offs—0
Connecting Multi-modal Contrastive Representations—0
Computation and Data Efficient Backdoor Attacks—0
Global Context Aware Convolutions for 3D Point Cloud Understanding—0
CLR-GAM: Contrastive Point Cloud Learning with Guided Augmentation and Feature Mapping—0
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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