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 1–10 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
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All datasetsPointCloud-CISPRS

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1OursAverage F182.8—Unverified