SOTAVerified

Point Cloud Classification

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

Papers

Showing 101110 of 265 papers

TitleStatusHype
Efficient Converted Spiking Neural Network for 3D and 2D Classification0
ViewNet: A Novel Projection-Based Backbone With View Pooling for Few-Shot Point Cloud ClassificationCode1
CAP: Robust Point Cloud Classification via Semantic and Structural Modeling0
Learnable Skeleton-Aware 3D Point Cloud Sampling0
Implicit Convolutional Kernels for Steerable CNNsCode1
Local Neighborhood Features for 3D ClassificationCode0
TetraSphere: A Neural Descriptor for O(3)-Invariant Point Cloud AnalysisCode1
PointCA: Evaluating the Robustness of 3D Point Cloud Completion Models Against Adversarial Examples0
MATE: Masked Autoencoders are Online 3D Test-Time LearnersCode1
Equivariance with Learned Canonicalization Functions0
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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