SOTAVerified

Point Cloud Classification

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

Papers

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Showing 1–50 of 265 papers

TitleStatusHype
BeyondRPC: A Contrastive and Augmentation-Driven Framework for Robust Point Cloud UnderstandingCode0
Rethinking Gradient-based Adversarial Attacks on Point Cloud Classification—0
SMART-PC: Skeletal Model Adaptation for Robust Test-Time Training in Point CloudsCode1
Optimal Control for Transformer Architectures: Enhancing Generalization, Robustness and Efficiency—0
Hybrid-Emba3D: Geometry-Aware and Cross-Path Feature Hybrid Enhanced State Space Model for Point Cloud ClassificationCode0
Streaming Sliced Optimal TransportCode0
FA-KPConv: Introducing Euclidean Symmetries to KPConv via Frame Averaging—0
DG-MVP: 3D Domain Generalization via Multiple Views of Point Clouds for Classification—0
Introducing the Short-Time Fourier Kolmogorov Arnold Network: A Dynamic Graph CNN Approach for Tree Species Classification in 3D Point CloudsCode0
Fourier Decomposition for Explicit Representation of 3D Point Cloud Attributes—0
RS2AD: End-to-End Autonomous Driving Data Generation from Roadside Sensor Observations—0
DDEQs: Distributional Deep Equilibrium Models through Wasserstein Gradient FlowsCode0
Spiking Point Transformer for Point Cloud ClassificationCode1
Token Adaptation via Side Graph Convolution for Temporally and Spatially Efficient Fine-tuning of 3D Point Cloud TransformersCode0
AIQViT: Architecture-Informed Post-Training Quantization for Vision Transformers—0
OT-Transformer: A Continuous-time Transformer Architecture with Optimal Transport Regularization—0
Point-LN: A Lightweight Framework for Efficient Point Cloud Classification Using Non-Parametric Positional EncodingCode0
RW-Net: Enhancing Few-Shot Point Cloud Classification with a Wavelet Transform Projection-based Network—0
Point-GN: A Non-Parametric Network Using Gaussian Positional Encoding for Point Cloud ClassificationCode1
STREAM: A Universal State-Space Model for Sparse Geometric Data—0
Test-Time Adaptation in Point Clouds: Leveraging Sampling Variation with Weight AveragingCode1
Low-Density 3D Point Cloud Classification—0
PointNet with KAN versus PointNet with MLP for 3D Classification and Segmentation of Point SetsCode1
Robust 3D Point Clouds Classification based on Declarative DefendersCode1
Parameter-Efficient Fine-Tuning in Spectral Domain for Point Cloud LearningCode3
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