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3D Point Cloud Classification
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논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Asymmetric Dual Self-Distillation for 3D Self-Supervised Representation Learning
Rethinking Gradient-based Adversarial Attacks on Point Cloud Classification
SMART-PC: Skeletal Model Adaptation for Robust Test-Time Training in Point Clouds
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
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
Rethinking Masked Representation Learning for 3D Point Cloud Understanding
Point-GR: Graph Residual Point Cloud Network for 3D Object Classification and Segmentation
Point-GN: A Non-Parametric Network Using Gaussian Positional Encoding for Point Cloud Classification
Efficient Spatio-Temporal Signal Recognition on Edge Devices Using PointLCA-Net
Test-Time Adaptation in Point Clouds: Leveraging Sampling Variation with Weight Averaging
Low-Density 3D Point Cloud Classification
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
MIRACLE 3D: Memory-efficient Integrated Robust Approach for Continual Learning on Point Clouds via Shape Model construction
3D-JEPA: A Joint Embedding Predictive Architecture for 3D Self-Supervised Representation Learning
Formula-Supervised Visual-Geometric Pre-training
Continual Learning in 3D Point Clouds: Employing Spectral Techniques for Exemplar Selection
GS-PT: Exploiting 3D Gaussian Splatting for Comprehensive Point Cloud Understanding via Self-supervised Learning
SA-MLP: A Low-Power Multiplication-Free Deep Network for 3D Point Cloud Classification in Resource-Constrained Environments
Positional Prompt Tuning for Efficient 3D Representation Learning
Temporal Reversed Training for Spiking Neural Networks with Generalized Spatio-Temporal Representation
PCP-MAE: Learning to Predict Centers for Point Masked Autoencoders
DC3DO: Diffusion Classifier for 3D Objects
RISurConv: Rotation Invariant Surface Attention-Augmented Convolutions for 3D Point Cloud Classification and Segmentation
GPSFormer: A Global Perception and Local Structure Fitting-based Transformer for Point Cloud Understanding
Transferable 3D Adversarial Shape Completion using Diffusion Models
Eidos: Efficient, Imperceptible Adversarial 3D Point Clouds
KPConvX: Modernizing Kernel Point Convolution with Kernel Attention
A comprehensive overview of deep learning techniques for 3D point cloud classification and semantic segmentation
FRACTAL: An Ultra-Large-Scale Aerial Lidar Dataset for 3D Semantic Segmentation of Diverse Landscapes
MiniGPT-3D: Efficiently Aligning 3D Point Clouds with Large Language Models using 2D Priors
Point-JEPA: A Joint Embedding Predictive Architecture for Self-Supervised Learning on Point Cloud
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