SOTAVerified

Point Cloud Classification

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

Papers

Showing 241250 of 265 papers

TitleStatusHype
Spherical Kernel for Efficient Graph Convolution on 3D Point CloudsCode0
Deep Declarative Networks: A New HopeCode1
Directionally Constrained Fully Convolutional Neural Network For Airborne Lidar Point Cloud ClassificationCode0
Adversarial shape perturbations on 3D point cloudsCode0
Revisiting Point Cloud Classification: A New Benchmark Dataset and Classification Model on Real-World DataCode1
PointHop: An Explainable Machine Learning Method for Point Cloud ClassificationCode1
ACNe: Attentive Context Normalization for Robust Permutation-Equivariant LearningCode0
Structural Relational Reasoning of Point Clouds0
NPTC-net: Narrow-Band Parallel Transport Convolutional Neural Network on Point Clouds0
Unsupervised Feature Learning for Point Cloud by Contrasting and Clustering With Graph Convolutional Neural NetworkCode0
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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