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Point Cloud Classification
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Point Cloud Classification is a task involving the classification of unordered 3D point sets (point clouds).
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Rethinking Gradient-based Adversarial Attacks on Point Cloud Classification
SMART-PC: Skeletal Model Adaptation for Robust Test-Time Training in Point Clouds
Hybrid-Emba3D: Geometry-Aware and Cross-Path Feature Hybrid Enhanced State Space Model for Point Cloud Classification
Optimal Control for Transformer Architectures: Enhancing Generalization, Robustness and Efficiency
Streaming Sliced Optimal Transport
FA-KPConv: Introducing Euclidean Symmetries to KPConv via Frame Averaging
DG-MVP: 3D Domain Generalization via Multiple Views of Point Clouds for Classification
Introducing the Short-Time Fourier Kolmogorov Arnold Network: A Dynamic Graph CNN Approach for Tree Species Classification in 3D Point Clouds
Fourier Decomposition for Explicit Representation of 3D Point Cloud Attributes
RS2AD: End-to-End Autonomous Driving Data Generation from Roadside Sensor Observations
Fast Jet Tagging with MLP-Mixers on FPGAs
DDEQs: Distributional Deep Equilibrium Models through Wasserstein Gradient Flows
Token Adaptation via Side Graph Convolution for Temporally and Spatially Efficient Fine-tuning of 3D Point Cloud Transformers
Spiking Point Transformer for Point Cloud Classification
AIQViT: Architecture-Informed Post-Training Quantization for Vision Transformers
OT-Transformer: A Continuous-time Transformer Architecture with Optimal Transport Regularization
Point-LN: A Lightweight Framework for Efficient Point Cloud Classification Using Non-Parametric Positional Encoding
RW-Net: Enhancing Few-Shot Point Cloud Classification with a Wavelet Transform Projection-based Network
Learning Symmetry-Independent Jet Representations via Jet-Based Joint Embedding Predictive Architecture
Point-GN: A Non-Parametric Network Using Gaussian Positional Encoding for Point Cloud Classification
Interpreting Transformers for Jet Tagging
Lie-Equivariant Quantum Graph Neural Networks
STREAM: A Universal State-Space Model for Sparse Geometric Data
Lorentz-Equivariant Quantum Graph Neural Network for High-Energy Physics
Quantum Rationale-Aware Graph Contrastive Learning for Jet Discrimination
Test-Time Adaptation in Point Clouds: Leveraging Sampling Variation with Weight Averaging
Low-Density 3D Point Cloud Classification
MACK: Mismodeling Addressed with Contrastive Knowledge
PointNet with KAN versus PointNet with MLP for 3D Classification and Segmentation of Point Sets
Robust 3D Point Clouds Classification based on Declarative Defenders
Parameter-Efficient Fine-Tuning in Spectral Domain for Point Cloud Learning
Curvature Diversity-Driven Deformation and Domain Alignment for Point Cloud
Bridging Domain Gap of Point Cloud Representations via Self-Supervised Geometric Augmentation
SA-MLP: A Low-Power Multiplication-Free Deep Network for 3D Point Cloud Classification in Resource-Constrained Environments
PMT-MAE: Dual-Branch Self-Supervised Learning with Distillation for Efficient Point Cloud Classification
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