3D Point Cloud Linear Classification on ModelNet40

Metric: Overall Accuracy (higher is better)

LeaderboardDataset

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#	Model↕	Overall Accuracy▼	Extra Data	Paper	Date↕	Code
1	ReCon++	93.6	No	ShapeLLM: Universal 3D Object Understanding for ...	2024-02-27	Code
2	I2P-MAE	93.4	Yes	Learning 3D Representations from 2D Pre-trained ...	2022-12-13	Code
3	ReCon	93.4	Yes	Contrast with Reconstruct: Contrastive 3D Repres...	2023-02-05	Code
4	Point-M2AE	92.9	Yes	Point-M2AE: Multi-scale Masked Autoencoders for ...	2022-05-28	Code
5	IAE (DGCNN)	92.1	Yes	Implicit Autoencoder for Point-Cloud Self-Superv...	2022-01-03	Code
6	AdaCrossNet	91.8	Yes	-	-	Code
7	CrossMoCo	91.49	No	-	-	Code
8	CrossPoint	91.2	Yes	CrossPoint: Self-Supervised Cross-Modal Contrast...	2022-03-01	Code
9	STRL	90.9	No	Spatio-temporal Self-Supervised Representation L...	2021-09-01	Code
10	PSG-Net	90.9	No	Progressive Seed Generation Auto-encoder for Uns...	2021-12-09	-
11	PointOE	90.7	No	Self-supervised Learning of Point Clouds via Ori...	2020-08-01	Code
12	Point-Jigsaw	90.6	No	Self-Supervised Deep Learning on Point Clouds by...	2019-01-24	-
13	MID-FC	90.3	No	Unsupervised 3D Learning for Shape Analysis via ...	2020-08-03	Code
14	VIP-GAN	90.2	No	View Inter-Prediction GAN: Unsupervised Represen...	2018-11-07	-
15	OcCo	89.2	No	Unsupervised Point Cloud Pre-Training via Occlus...	2020-10-02	Code
16	FoldingNet	88.4	No	FoldingNet: Point Cloud Auto-encoder via Deep Gr...	2017-12-19	Code
17	MAE-VAE	88.4	No	Multi-Angle Point Cloud-VAE: Unsupervised Featur...	2019-07-30	-
18	SO-Net	87.5	No	SO-Net: Self-Organizing Network for Point Cloud ...	2018-03-12	Code
19	3D-GAN	83.3	No	Learning a Probabilistic Latent Space of Object ...	2016-10-24	Code