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 26–50 of 265 papers

TitleStatusHype
Curvature Diversity-Driven Deformation and Domain Alignment for Point CloudCode2
Bridging Domain Gap of Point Cloud Representations via Self-Supervised Geometric Augmentation—0
SA-MLP: A Low-Power Multiplication-Free Deep Network for 3D Point Cloud Classification in Resource-Constrained EnvironmentsCode0
PMT-MAE: Dual-Branch Self-Supervised Learning with Distillation for Efficient Point Cloud Classification—0
Efficient Point Cloud Classification via Offline Distillation Framework and Negative-Weight Self-Distillation Technique—0
PointDGMamba: Domain Generalization of Point Cloud Classification via Generalized State Space ModelCode0
Enhancing Sampling Protocol for Point Cloud Classification Against Corruptions—0
Positional Prompt Tuning for Efficient 3D Representation LearningCode1
Temporal Reversed Training for Spiking Neural Networks with Generalized Spatio-Temporal Representation—0
RISurConv: Rotation Invariant Surface Attention-Augmented Convolutions for 3D Point Cloud Classification and SegmentationCode1
CLIP-based Point Cloud Classification via Point Cloud to Image Translation—0
Rethinking Attention Module Design for Point Cloud Analysis—0
Boosting Cross-Domain Point Classification via Distilling Relational Priors from 2D TransformersCode0
Transferable 3D Adversarial Shape Completion using Diffusion ModelsCode0
Eidos: Efficient, Imperceptible Adversarial 3D Point Clouds—0
A comprehensive overview of deep learning techniques for 3D point cloud classification and semantic segmentation—0
FRACTAL: An Ultra-Large-Scale Aerial Lidar Dataset for 3D Semantic Segmentation of Diverse LandscapesCode3
Leveraging PointNet and PointNet++ for Lyft Point Cloud Classification Challenge—0
CloudFort: Enhancing Robustness of 3D Point Cloud Classification Against Backdoor Attacks via Spatial Partitioning and Ensemble Prediction—0
A Hybrid Generative and Discriminative PointNet on Unordered Point Sets—0
Meta Episodic learning with Dynamic Task Sampling for CLIP-based Point Cloud Classification—0
Image and Point-cloud Classification for Jet Analysis in High-Energy Physics: A survey—0
FBPT: A Fully Binary Point Transformer—0
Classifying Objects in 3D Point Clouds Using Recurrent Neural Network: A GRU LSTM Hybrid ApproachCode0
Hide in Thicket: Generating Imperceptible and Rational Adversarial Perturbations on 3D Point CloudsCode1
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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