3D Point Cloud Linear Classification On

Metrics

Overall Accuracy

Results

Performance results of various models on this benchmark

		Paper Title	Repository
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

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3D Point Cloud Linear Classification On

Metrics

Results