SOTAVerified

Point Cloud Classification

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

Papers

Showing 231240 of 265 papers

TitleStatusHype
Efficient and Stable Graph Scattering Transforms via Pruning0
Geometry Sharing Network for 3D Point Cloud Classification and SegmentationCode0
Transductive Zero-Shot Learning for 3D Point Cloud ClassificationCode1
Grid-GCN for Fast and Scalable Point Cloud LearningCode0
SGAS: Sequential Greedy Architecture SearchCode0
Geometric Back-projection Network for Point Cloud ClassificationCode0
Learning Permutation Invariant Representations using Memory NetworksCode0
SRINet: Learning Strictly Rotation-Invariant Representations for Point Cloud Classification and Segmentation0
Rotation Invariant Point Cloud Classification: Where Local Geometry Meets Global TopologyCode0
Density-Aware Convolutional Networks with Context Encoding for Airborne LiDAR Point Cloud Classification0
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Benchmark Results

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