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Unsupervised 3D Point Cloud Linear Evaluation

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Training a linear classifier(e.g. SVM) on the representations learned in an unsupervised manner on the pretrained(e.g. ShapeNet) dataset.

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/OmidPoursaeed/Self_supervised_Learning_Point_Clouds

공식 코드

Self-supervised Learning of Point Clouds via Orientation Estimation

https://github.com/hansen7/OcCo

공식 코드

Unsupervised Point Cloud Pre-Training via Occlusion Completion

https://github.com/lijx10/SO-Net

공식 코드

SO-Net: Self-Organizing Network for Point Cloud Analysis

https://github.com/yichen928/STRL

공식 코드

Spatio-temporal Self-Supervised Representation Learning for 3D Point Clouds

https://github.com/AnTao97/UnsupervisedPointCloudReconstruction

FoldingNet: Point Cloud Auto-encoder via Deep Grid Deformation

https://github.com/LONG-9621/SO-Net

SO-Net: Self-Organizing Network for Point Cloud Analysis

https://github.com/XuyangBai/FoldingNet

FoldingNet: Point Cloud Auto-encoder via Deep Grid Deformation

https://github.com/black0017/3D-GAN-pytorch

Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling

https://github.com/chinokenochkan/3dgan-keras

Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling

https://github.com/donnyruixu/pc-elm-ae

SO-Net: Self-Organizing Network for Point Cloud Analysis

https://github.com/qinglew/FoldingNet

FoldingNet: Point Cloud Auto-encoder via Deep Grid Deformation

https://github.com/zck119/3dgan-release

Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling

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