Unsupervised 3D Point Cloud Linear Evaluation
6 papers with code • 0 benchmarks • 0 datasets
Training a linear classifier(e.g. SVM) on the representations learned in an unsupervised manner on the pretrained(e.g. ShapeNet) dataset.
Benchmarks
These leaderboards are used to track progress in Unsupervised 3D Point Cloud Linear Evaluation
Most implemented papers
Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling
zck119/3dgan-release
•
•
NeurIPS 2016
We study the problem of 3D object generation.
FoldingNet: Point Cloud Auto-encoder via Deep Grid Deformation
AnTao97/UnsupervisedPointCloudReconstruction
•
•
CVPR 2018
Recent deep networks that directly handle points in a point set, e. g., PointNet, have been state-of-the-art for supervised learning tasks on point clouds such as classification and segmentation.
SO-Net: Self-Organizing Network for Point Cloud Analysis
This paper presents SO-Net, a permutation invariant architecture for deep learning with orderless point clouds.
Self-supervised Learning of Point Clouds via Orientation Estimation
OmidPoursaeed/Self_supervised_Learning_Point_Clouds
•
•
A point cloud can be rotated in infinitely many ways, which provides a rich label-free source for self-supervision.
Unsupervised Point Cloud Pre-Training via Occlusion Completion
We find that even when we construct a single pre-training dataset (from ModelNet40), this pre-training method improves accuracy across different datasets and encoders, on a wide range of downstream tasks.
Spatio-temporal Self-Supervised Representation Learning for 3D Point Clouds
To date, various 3D scene understanding tasks still lack practical and generalizable pre-trained models, primarily due to the intricate nature of 3D scene understanding tasks and their immense variations introduced by camera views, lighting, occlusions, etc.
