3D AffordanceNet
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3D AffordanceNet是一个大规模的3D点云数据集，由华南理工大学创建，包含22949个形状，涵盖23个语义对象类别，并标注了18种视觉可操作性类别。数据集通过密集采样点并使用标签传播方法获得地面实况数据。该数据集支持多种任务，包括全形状、部分视图和旋转不变性可操作性估计，旨在推动视觉引导机器人研究中的可操作性理解。
3D AffordanceNet is a large-scale 3D point cloud dataset created by South China University of Technology. It contains 22,949 shape instances covering 23 semantic object categories and annotated with 18 visual affordance categories. Ground truth data of this dataset is obtained via dense point sampling and label propagation methods. This dataset supports multiple tasks including full-shape, partial-view and rotation-invariant affordance estimation, aiming to advance affordance understanding in vision-guided robotics research.
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华南理工大学创建时间：
2021-03-30
