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3D Point Cloud Linear Classification
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Training a linear classifier(e.g. SVM) on the embeddings/representations of 3D point clouds. The embeddings/representations are usually trained in an unsupervised manner.
논문
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ShapeLLM: Universal 3D Object Understanding for Embodied Interaction
CrossMoCo: Multi-modal Momentum Contrastive Learning for Point Cloud
Contrast with Reconstruct: Contrastive 3D Representation Learning Guided by Generative Pretraining
Learning 3D Representations from 2D Pre-trained Models via Image-to-Point Masked Autoencoders
Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-training
CrossPoint: Self-Supervised Cross-Modal Contrastive Learning for 3D Point Cloud Understanding
Implicit Autoencoder for Point-Cloud Self-Supervised Representation Learning
Progressive Seed Generation Auto-encoder for Unsupervised Point Cloud Learning
Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point Modeling
Spatio-temporal Self-Supervised Representation Learning for 3D Point Clouds
Unsupervised Point Cloud Pre-Training via Occlusion Completion
Unsupervised 3D Learning for Shape Analysis via Multiresolution Instance Discrimination
Self-supervised Learning of Point Clouds via Orientation Estimation
Multi-Angle Point Cloud-VAE: Unsupervised Feature Learning for 3D Point Clouds from Multiple Angles by Joint Self-Reconstruction and Half-to-Half Prediction
Self-Supervised Deep Learning on Point Clouds by Reconstructing Space
View Inter-Prediction GAN: Unsupervised Representation Learning for 3D Shapes by Learning Global Shape Memories to Support Local View Predictions
SO-Net: Self-Organizing Network for Point Cloud Analysis
FoldingNet: Point Cloud Auto-encoder via Deep Grid Deformation
Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling
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