Few-Shot 3D Point Cloud Classification

GPr-Net: Geometric Prototypical Network for Point Cloud Few-Shot Learning

2304.06007 2023-04-12 이 페이지의 평가 4건

Overall Accuracy: 73.8Standard Deviation: 2.0

Self-Supervised Few-Shot Learning on Point Clouds

2009.14168 2020-09-29 이 페이지의 평가 2건

Overall Accuracy: 53.00Standard Deviation: 4.1

Unsupervised Point Cloud Pre-Training via Occlusion Completion

2010.01089 2020-10-02 이 페이지의 평가 2건

Overall Accuracy: 89.7Standard Deviation: 1.5

3D-JEPA: A Joint Embedding Predictive Architecture for 3D Self-Supervised Representation Learning

2409.15803 2024-09-24 이 페이지의 평가 1건

Overall Accuracy: 96.3Standard Deviation: 2.4

Autoencoders as Cross-Modal Teachers: Can Pretrained 2D Image Transformers Help 3D Representation Learning?

2212.08320 2022-12-16 이 페이지의 평가 1건

Overall Accuracy: 95.6Standard Deviation: 2.8

Contrast with Reconstruct: Contrastive 3D Representation Learning Guided by Generative Pretraining

2302.02318 2023-02-05 이 페이지의 평가 1건

Overall Accuracy: 95.8Standard Deviation: 3.0

CrossMoCo: Multi-modal Momentum Contrastive Learning for Point Cloud

2023-06-08 이 페이지의 평가 1건

Overall Accuracy: 91.0Standard Deviation: 3.4

Instance-aware Dynamic Prompt Tuning for Pre-trained Point Cloud Models

2304.07221 2023-04-14 이 페이지의 평가 1건

Overall Accuracy: 95.4

Learning 3D Representations from 2D Pre-trained Models via Image-to-Point Masked Autoencoders

2212.06785 2022-12-13 이 페이지의 평가 1건

Overall Accuracy: 95.5Standard Deviation: 3.0

Masked Autoencoders for Point Cloud Self-supervised Learning

2203.06604 2022-03-13 이 페이지의 평가 1건

Overall Accuracy: 95.0Standard Deviation: 3.0

Masked Discrimination for Self-Supervised Learning on Point Clouds

2203.11183 2022-03-21 이 페이지의 평가 1건

Overall Accuracy: 93.4Standard Deviation: 3.5

PCP-MAE: Learning to Predict Centers for Point Masked Autoencoders

2408.08753 2024-08-16 이 페이지의 평가 1건

Overall Accuracy: 95.9Standard Deviation: 2.7

Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point Modeling

2111.14819 2021-11-29 이 페이지의 평가 1건

Overall Accuracy: 92.7Standard Deviation: 5.1

Point-JEPA: A Joint Embedding Predictive Architecture for Self-Supervised Learning on Point Cloud

2404.16432 2024-04-25 이 페이지의 평가 1건

Overall Accuracy: 96.4Standard Deviation: 2.7

Point-LGMask: Local and Global Contexts Embedding for Point Cloud Pre-training with Multi-Ratio Masking

2023-06-08 이 페이지의 평가 1건

Overall Accuracy: 95.1Standard Deviation: 3.4

Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-training

2205.14401 2022-05-28 이 페이지의 평가 1건

Overall Accuracy: 95.0Standard Deviation: 3.0

Point2Vec for Self-Supervised Representation Learning on Point Clouds

2303.16570 2023-03-29 이 페이지의 평가 1건

Overall Accuracy: 95.8Standard Deviation: 3.1

PointCNN: Convolution On X-Transformed Points

2018-12-01 이 페이지의 평가 1건

Overall Accuracy: 49.95Standard Deviation: 7.2

PointGPT: Auto-regressively Generative Pre-training from Point Clouds

2305.11487 2023-05-19 이 페이지의 평가 1건

Overall Accuracy: 96.1Standard Deviation: 2.8

PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation

1612.00593 2016-12-02 이 페이지의 평가 1건

Overall Accuracy: 35.20Standard Deviation: 13.5

PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space

1706.02413 2017-06-07 이 페이지의 평가 1건

Overall Accuracy: 18.80Standard Deviation: 7.0

Regress Before Construct: Regress Autoencoder for Point Cloud Self-supervised Learning

2310.03670 2023-09-25 이 페이지의 평가 1건

Overall Accuracy: 95.8Standard Deviation: 3.0

Rethinking Masked Representation Learning for 3D Point Cloud Understanding

2024-12-26 이 페이지의 평가 1건

Overall Accuracy: 95.6Standard Deviation: 2.6

ShapeLLM: Universal 3D Object Understanding for Embodied Interaction

2402.17766 2024-02-27 이 페이지의 평가 1건

Overall Accuracy: 96.5Standard Deviation: 3.0

Towards Compact 3D Representations via Point Feature Enhancement Masked Autoencoders

2312.10726 2023-12-17 이 페이지의 평가 1건

Overall Accuracy: 95.8

Few-Shot 3D Point Cloud Classification

논문