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
PMT-MAE: Dual-Branch Self-Supervised Learning with Distillation for Efficient Point Cloud Classification—0
Shape-Oriented Convolution Neural Network for Point Cloud Analysis—0
PointCA: Evaluating the Robustness of 3D Point Cloud Completion Models Against Adversarial Examples—0
PointCert: Point Cloud Classification with Deterministic Certified Robustness Guarantees—0
Transformers in Vision: A Survey—0
Point Cloud Classification via Deep Set Linearized Optimal Transport—0
Sparse Convolutions on Continuous Domains for Point Cloud and Event Stream Networks—0
Point Discriminative Learning for Data-efficient 3D Point Cloud Analysis—0
EPiC: Ensemble of Partial Point Clouds for Robust ClassificationCode0
Multi-scale Receptive Fields Graph Attention Network for Point Cloud ClassificationCode0
Equivariant vs. Invariant Layers: A Comparison of Backbone and Pooling for Point Cloud ClassificationCode0
Classifying Objects in 3D Point Clouds Using Recurrent Neural Network: A GRU LSTM Hybrid ApproachCode0
Two Heads are Better than One: Geometric-Latent Attention for Point Cloud Classification and SegmentationCode0
Point-PlaneNet: Plane kernel based convolutional neural network for point clouds analysisCode0
Point-Voxel Adaptive Feature Abstraction for Robust Point Cloud ClassificationCode0
On Automatic Data Augmentation for 3D Point Cloud ClassificationCode0
Classifying point clouds at the facade-level using geometric features and deep learning networksCode0
3D Point Cloud Classification and Segmentation using 3D Modified Fisher Vector Representation for Convolutional Neural NetworksCode0
DDEQs: Distributional Deep Equilibrium Models through Wasserstein Gradient FlowsCode0
RefRec: Pseudo-labels Refinement via Shape Reconstruction for Unsupervised 3D Domain AdaptationCode0
ACNe: Attentive Context Normalization for Robust Permutation-Equivariant LearningCode0
Directionally Constrained Fully Convolutional Neural Network For Airborne Lidar Point Cloud ClassificationCode0
SA-MLP: A Low-Power Multiplication-Free Deep Network for 3D Point Cloud Classification in Resource-Constrained EnvironmentsCode0
Hybrid-Emba3D: Geometry-Aware and Cross-Path Feature Hybrid Enhanced State Space Model for Point Cloud ClassificationCode0
Token Adaptation via Side Graph Convolution for Temporally and Spatially Efficient Fine-tuning of 3D Point Cloud TransformersCode0
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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