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 201–225 of 265 papers

TitleStatusHype
Point Discriminative Learning for Data-efficient 3D Point Cloud Analysis—0
Rotation Transformation Network: Learning View-Invariant Point Cloud for Classification and SegmentationCode0
Training or Architecture? How to Incorporate Invariance in Neural Networks—0
Towards Training Stronger Video Vision Transformers for EPIC-KITCHENS-100 Action Recognition—0
Dual Transformer for Point Cloud Analysis—0
A Learnable Self-supervised Task for Unsupervised Domain Adaptation on Point Clouds—0
FatNet: A Feature-attentive Network for 3D Point Cloud Processing—0
Few-Data Guided Learning Upon End-to-End Point Cloud Network for 3D Face Recognition—0
PointShuffleNet: Learning Non-Euclidean Features with Homotopy Equivalence and Mutual Information—0
PointGuard: Provably Robust 3D Point Cloud Classification—0
EllipsoidNet: Ellipsoid Representation for Point Cloud Classification and Segmentation—0
Transformers in Vision: A Survey—0
Learning Rotation-Invariant Representations of Point Clouds Using Aligned Edge Convolutional Neural Networks—0
The Card Shuffling Hypotheses: Building a Time and Memory Efficient Graph Convolutional Network—0
DeeperGCN: Training Deeper GCNs with Generalized Aggregation Functions—0
GraNet: Global Relation-aware Attentional Network for ALS Point Cloud Classification—0
Classification of Single-View Object Point Clouds—0
Learning Category-level Shape Saliency via Deep Implicit Surface Networks—0
On Learning the Right Attention Point for Feature Enhancement—0
Sparse Convolutions on Continuous Domains for Point Cloud and Event Stream Networks—0
On Adversarial Robustness of 3D Point Cloud Classification under Adaptive Attacks—0
MGIC: Multigrid-in-Channels Neural Network ArchitecturesCode0
Empowering Knowledge Distillation via Open Set Recognition for Robust 3D Point Cloud Classification—0
PointManifold: Using Manifold Learning for Point Cloud Classification—0
On The Adversarial Robustness of 3D Point Cloud Classification—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