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المنصة
الرئيسية
SOTA
تصنيف السحابة النقطية ثلاثية الأبعاد بقليل من العينات
Few Shot 3D Point Cloud Classification On 3
Few Shot 3D Point Cloud Classification On 3
المقاييس
Overall Accuracy
Standard Deviation
النتائج
نتائج أداء النماذج المختلفة على هذا المعيار القياسي
Columns
اسم النموذج
Overall Accuracy
Standard Deviation
Paper Title
Point-JEPA
95.0
3.6
Point-JEPA: A Joint Embedding Predictive Architecture for Self-Supervised Learning on Point Cloud
ReCon++
94.5
4.1
ShapeLLM: Universal 3D Object Understanding for Embodied Interaction
3D-JEPA
94.3
3.6
3D-JEPA: A Joint Embedding Predictive Architecture for 3D Self-Supervised Representation Learning
PointGPT
94.3
3.3
PointGPT: Auto-regressively Generative Pre-training from Point Clouds
Point-FEMAE
94.0
-
Towards Compact 3D Representations via Point Feature Enhancement Masked Autoencoders
point2vec
93.9
4.1
Point2Vec for Self-Supervised Representation Learning on Point Clouds
PCP-MAE
93.5
3.7
PCP-MAE: Learning to Predict Centers for Point Masked Autoencoders
Point-RAE
93.3
4.0
Regress Before Construct: Regress Autoencoder for Point Cloud Self-supervised Learning
ReCon
93.3
3.9
Contrast with Reconstruct: Contrastive 3D Representation Learning Guided by Generative Pretraining
ACT
93.3
4.0
Autoencoders as Cross-Modal Teachers: Can Pretrained 2D Image Transformers Help 3D Representation Learning?
OTMae3D
93.2
3.4
-
IDPT
92.8
-
Instance-aware Dynamic Prompt Tuning for Pre-trained Point Cloud Models
Point-LGMask
92.6
4.3
Point-LGMask: Local and Global Contexts Embedding for Point Cloud Pre-training with Multi-Ratio Masking
Point-MAE
92.6
4.1
Masked Autoencoders for Point Cloud Self-supervised Learning
I2P-MAE
92.6
5.0
Learning 3D Representations from 2D Pre-trained Models via Image-to-Point Masked Autoencoders
Point-M2AE
92.3
4.5
Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-training
MaskPoint
91.4
4.0
Masked Discrimination for Self-Supervised Learning on Point Clouds
Point-BERT
91.0
5.4
Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point Modeling
CrossMoCo
88.7
3.9
CrossMoCo: Multi-modal Momentum Contrastive Learning for Point Cloud
OcCo+PointNet
83.9
1.8
Unsupervised Point Cloud Pre-Training via Occlusion Completion
0 of 31 row(s) selected.
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