HyperAI
HyperAI
Accueil
Actualités
Articles de recherche récents
Tutoriels
Ensembles de données
Wiki
SOTA
Modèles LLM
Classement GPU
Événements
Recherche
À propos
Français
HyperAI
HyperAI
Toggle sidebar
Rechercher sur le site...
⌘
K
Accueil
SOTA
Classification linéaire de nuage de points 3D
3D Point Cloud Linear Classification On
3D Point Cloud Linear Classification On
Métriques
Overall Accuracy
Résultats
Résultats de performance de divers modèles sur ce benchmark
Columns
Nom du modèle
Overall Accuracy
Paper Title
Repository
IAE (DGCNN)
92.1
Implicit Autoencoder for Point-Cloud Self-Supervised Representation Learning
-
I2P-MAE
93.4
Learning 3D Representations from 2D Pre-trained Models via Image-to-Point Masked Autoencoders
-
AdaCrossNet
91.4
AdaCrossNet: Adaptive Dynamic Loss Weighting for Cross-Modal Contrastive Point Cloud Learning
ReCon
93.4
Contrast with Reconstruct: Contrastive 3D Representation Learning Guided by Generative Pretraining
-
OcCo
89.2
Unsupervised Point Cloud Pre-Training via Occlusion Completion
-
CrossPoint
91.2
CrossPoint: Self-Supervised Cross-Modal Contrastive Learning for 3D Point Cloud Understanding
-
MAE-VAE
88.4
Multi-Angle Point Cloud-VAE: Unsupervised Feature Learning for 3D Point Clouds from Multiple Angles by Joint Self-Reconstruction and Half-to-Half Prediction
-
Point-M2AE
92.9
Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-training
-
FoldingNet
88.4
FoldingNet: Point Cloud Auto-encoder via Deep Grid Deformation
-
PointOE
90.7
Self-supervised Learning of Point Clouds via Orientation Estimation
-
Point-Jigsaw
90.6
Self-Supervised Deep Learning on Point Clouds by Reconstructing Space
-
MID-FC
90.3
Unsupervised 3D Learning for Shape Analysis via Multiresolution Instance Discrimination
-
SO-Net
87.5
SO-Net: Self-Organizing Network for Point Cloud Analysis
-
STRL
90.9
Spatio-temporal Self-Supervised Representation Learning for 3D Point Clouds
-
PSG-Net
90.9
Progressive Seed Generation Auto-encoder for Unsupervised Point Cloud Learning
-
3D-GAN
83.3
Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling
-
Point-JEPA
93.7±0.2
Point-JEPA: A Joint Embedding Predictive Architecture for Self-Supervised Learning on Point Cloud
-
ReCon++
93.6
ShapeLLM: Universal 3D Object Understanding for Embodied Interaction
-
VIP-GAN
90.2
View Inter-Prediction GAN: Unsupervised Representation Learning for 3D Shapes by Learning Global Shape Memories to Support Local View Predictions
-
CrossMoCo
91.49
CrossMoCo: Multi-modal Momentum Contrastive Learning for Point Cloud
0 of 20 row(s) selected.
Previous
Next