HyperAI
HyperAI超神経
ホーム
プラットフォーム
ドキュメント
ニュース
論文
チュートリアル
データセット
百科事典
SOTA
LLMモデル
GPU ランキング
学会
検索
サイトについて
利用規約
プライバシーポリシー
日本語
HyperAI
HyperAI超神経
Toggle Sidebar
サイトを検索…
⌘
K
Command Palette
Search for a command to run...
プラットフォーム
ホーム
SOTA
3Dポイントクラウド線形分類
3D Point Cloud Linear Classification On
3D Point Cloud Linear Classification On
評価指標
Overall Accuracy
評価結果
このベンチマークにおける各モデルのパフォーマンス結果
Columns
モデル名
Overall Accuracy
Paper Title
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
I2P-MAE
93.4
Learning 3D Representations from 2D Pre-trained Models via Image-to-Point Masked Autoencoders
ReCon
93.4
Contrast with Reconstruct: Contrastive 3D Representation Learning Guided by Generative Pretraining
Point-M2AE
92.9
Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-training
IAE (DGCNN)
92.1
Implicit Autoencoder for Point-Cloud Self-Supervised Representation Learning
CrossMoCo
91.49
CrossMoCo: Multi-modal Momentum Contrastive Learning for Point Cloud
AdaCrossNet
91.4
AdaCrossNet: Adaptive Dynamic Loss Weighting for Cross-Modal Contrastive Point Cloud Learning
CrossPoint
91.2
CrossPoint: Self-Supervised Cross-Modal Contrastive Learning for 3D Point Cloud Understanding
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
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
VIP-GAN
90.2
View Inter-Prediction GAN: Unsupervised Representation Learning for 3D Shapes by Learning Global Shape Memories to Support Local View Predictions
OcCo
89.2
Unsupervised Point Cloud Pre-Training via Occlusion Completion
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
FoldingNet
88.4
FoldingNet: Point Cloud Auto-encoder via Deep Grid Deformation
SO-Net
87.5
SO-Net: Self-Organizing Network for Point Cloud Analysis
3D-GAN
83.3
Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling
0 of 20 row(s) selected.
Previous
Next