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ResearchFormerly WizWand

3D Object Classification on ModelNet40 8k P v1

94.7Overall AccuracyReCon
90.64491.69792.7593.803Jul 28, 2023Dec 19, 2023May 11, 2024Oct 2, 2024Feb 23, 2025Jul 17, 2025Dec 9, 2025
Updated 8mo ago

Evaluation results

MethodLinks
ReCon#P (M)=43.6, #F (G)=5.3, Training Paradigm=with Pretrained Cross-Modal Teacher Representation Learning2023.0794.7——
ReCon2025.1294.7——
VPP w/ vot.#P (M)=22.1, #F (G)=4.8, Training Paradigm=with Self-Supervised Representation Learning, Voting Strategy=Yes2023.0794.3——
Point-PQAE2025.1294.3——
CSCon2025.1294.3——
PointGPT2025.1294.2——
Point-MAE#P (M)=22.1, #F (G)=4.8, Training Paradigm=with Self-Supervised Representation Learning2023.0794——
VPP w/o vot.#P (M)=22.1, #F (G)=4.8, Training Paradigm=with Self-Supervised Representation Learning, Voting Strategy=No2023.0794——
ACT#P (M)=22.1, #F (G)=4.8, Training Paradigm=with Pretrained Cross-Modal Teacher Representation Learning2023.0794——
Point-MAE2025.1294——
ACT2025.1294——
Point-BERT#P (M)=22.1, #F (G)=4.8, Training Paradigm=with Self-Supervised Representation Learning2023.0793.8——
Point-BERT2025.1293.8——
PointNet++#P (M)=1.5, #F (G)=1.7, Training Paradigm=Supervised Learning Only2023.0791.9——
PointNet++2025.1291.9——
Transformer#P (M)=22.1, #F (G)=4.8, Training Paradigm=with Self-Supervised Representation Learning2023.0791.8——
PointNet#P (M)=3.5, #F (G)=0.5, Training Paradigm=Supervised Learning Only2023.0790.8——
PointNet2025.1290.8——
ACTReference=ICLR 23, Params. (M)=22.1, Training Protocol=Self-Supervised Representation Learning (FULL)2024.08—94—
DAPTReference=CVPR 24, Params. (M)=1.1, Baseline=Point-MAE, Training Protocol=Parameter-Efficient Supervised Fine-tuning2024.08—93.2793.05
DAPTReference=CVPR 24, Params. (M)=1.1, Baseline=RECON, Training Protocol=Parameter-Efficient Supervised Fine-tuning2024.08—93.0792.79
IDPTReference=ICCV 23, Params. (M)=1.7, Baseline=Point-MAE, Training Protocol=Parameter-Efficient Supervised Fine-tuning2024.08—93.8893.67
IDPTReference=ICCV 23, Params. (M)=1.7, Baseline=RECON, Training Protocol=Parameter-Efficient Supervised Fine-tuning2024.08—93.6493.52
OcCoReference=ICCV 21, Params. (M)=22.1, Training Protocol=Self-Supervised Representation Learning (FULL)2024.08——92.1
Point-BERTReference=CVPR 22, Params. (M)=22.1, Training Protocol=Self-Supervised Representation Learning (FULL)2024.08—93.8—
Point-MAEReference=ECCV 22, Params. (M)=22.1, Training Protocol=Self-Supervised Representation Learning (FULL)2024.08—94—
Point-MAE (Baseline)Reference=ECCV 22, Params. (M)=22.1, Training Protocol=Parameter-Efficient Supervised Fine-tuning2024.08—94—
PointNetReference=CVPR 17, Params. (M)=3.5, Training Protocol=Supervised Learning Only2024.08—90.8—
PointNet++Reference=NeurIPS 17, Params. (M)=1.5, Training Protocol=Supervised Learning Only2024.08—91.9—
PPTReference=ACMMM 25, Params. (M)=1.1, Baseline=Point-MAE, Training Protocol=Parameter-Efficient Supervised Fine-tuning2024.08—93.8893.51
PPTReference=ACMMM 25, Params. (M)=1.1, Baseline=RECON, Training Protocol=Parameter-Efficient Supervised Fine-tuning2024.08—93.8493.66
RECONReference=ICML 23, Params. (M)=44.3, Training Protocol=Self-Supervised Representation Learning (FULL)2024.08—94.3—
RECON (Baseline)Reference=ICML 23, Params. (M)=22.1, Training Protocol=Parameter-Efficient Supervised Fine-tuning2024.08—93—
VPPReference=NeurIPS 23, Params. (M)=22.1, Training Protocol=Self-Supervised Representation Learning (FULL)2024.08—94.3—