HANDAL：一个包含姿势标注、可利用性和重建的真实世界可操作物体类别数据集。

摘要

我们提出了HANDAL数据集，用于类别级别的物体姿态估计和可供性预测。与先前的数据集不同，我们的数据集专注于适合机器人执行器进行功能性抓取的机器人可操作对象，如钳子、器具和螺丝刀。我们的注释过程经过了简化，只需要一个现成的摄像头和半自动处理，即可生成高质量的3D注释，无需众包。该数据集包含来自212个真实世界对象的17个类别中2.2k个视频的308k个带注释的图像帧。我们专注于硬件和厨房工具对象，以促进研究实际场景中的实用性，其中机器人执行器需要与环境进行互动，而不仅仅是简单推动或不加选择地抓取。我们概述了我们的数据集在6自由度类别级别姿态+尺度估计及相关任务中的实用性。我们还提供了所有对象的3D重建网格，并概述了一些需要解决的瓶颈，以推动像这样的数据集的收集民主化。

English

We present the HANDAL dataset for category-level object pose estimation and affordance prediction. Unlike previous datasets, ours is focused on robotics-ready manipulable objects that are of the proper size and shape for functional grasping by robot manipulators, such as pliers, utensils, and screwdrivers. Our annotation process is streamlined, requiring only a single off-the-shelf camera and semi-automated processing, allowing us to produce high-quality 3D annotations without crowd-sourcing. The dataset consists of 308k annotated image frames from 2.2k videos of 212 real-world objects in 17 categories. We focus on hardware and kitchen tool objects to facilitate research in practical scenarios in which a robot manipulator needs to interact with the environment beyond simple pushing or indiscriminate grasping. We outline the usefulness of our dataset for 6-DoF category-level pose+scale estimation and related tasks. We also provide 3D reconstructed meshes of all objects, and we outline some of the bottlenecks to be addressed for democratizing the collection of datasets like this one.

HANDAL：一个包含姿势标注、可利用性和重建的真实世界可操作物体类别数据集。

HANDAL: A Dataset of Real-World Manipulable Object Categories with Pose Annotations, Affordances, and Reconstructions

摘要

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