xiaoaoran/3d_url_survey
(TPAMI2023) Unsupervised Point Cloud Representation Learning with Deep Neural Networks: A Survey
This resource helps researchers and practitioners understand the latest advancements in deep learning for interpreting 3D point cloud data without requiring manual labeling. It provides a comprehensive overview of various methods, what goes into them (unlabeled 3D point clouds), and what comes out (meaningful feature representations of the 3D data). This is ideal for anyone working with 3D sensor data in fields like robotics, autonomous vehicles, or architectural scanning.
212 stars. No commits in the last 6 months.
Use this if you need to quickly get up to speed on the state-of-the-art in unsupervised representation learning for 3D point clouds and identify suitable techniques for your unlabelled 3D datasets.
Not ideal if you are looking for ready-to-use software or code implementations without needing to understand the underlying research and methods.
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Apr 12, 2023
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