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Few-Shot 3D Point Cloud Classification
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이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Rethinking Masked Representation Learning for 3D Point Cloud Understanding
3D-JEPA: A Joint Embedding Predictive Architecture for 3D Self-Supervised Representation Learning
PCP-MAE: Learning to Predict Centers for Point Masked Autoencoders
Point-JEPA: A Joint Embedding Predictive Architecture for Self-Supervised Learning on Point Cloud
ShapeLLM: Universal 3D Object Understanding for Embodied Interaction
Towards Compact 3D Representations via Point Feature Enhancement Masked Autoencoders
Regress Before Construct: Regress Autoencoder for Point Cloud Self-supervised Learning
Point-LGMask: Local and Global Contexts Embedding for Point Cloud Pre-training with Multi-Ratio Masking
CrossMoCo: Multi-modal Momentum Contrastive Learning for Point Cloud
PointGPT: Auto-regressively Generative Pre-training from Point Clouds
Multi-view Vision-Prompt Fusion Network: Can 2D Pre-trained Model Boost 3D Point Cloud Data-scarce Learning?
Instance-aware Dynamic Prompt Tuning for Pre-trained Point Cloud Models
GPr-Net: Geometric Prototypical Network for Point Cloud Few-Shot Learning
A Closer Look at Few-Shot 3D Point Cloud Classification
Contrast with Reconstruct: Contrastive 3D Representation Learning Guided by Generative Pretraining
Autoencoders as Cross-Modal Teachers: Can Pretrained 2D Image Transformers Help 3D Representation Learning?
Learning 3D Representations from 2D Pre-trained Models via Image-to-Point Masked Autoencoders
Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-training
Masked Discrimination for Self-Supervised Learning on Point Clouds
Masked Autoencoders for Point Cloud Self-supervised Learning
CrossPoint: Self-Supervised Cross-Modal Contrastive Learning for 3D Point Cloud Understanding
Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point Modeling
Unsupervised Point Cloud Pre-Training via Occlusion Completion
Self-Supervised Few-Shot Learning on Point Clouds
Dynamic Graph CNN for Learning on Point Clouds
PointCNN: Convolution On $\mathcal{X}$-Transformed Points
Attention Is All You Need
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.