来自多个视角的物体集合的RGB-D图像数据集与对齐的物体高分辨率3D模型的数据集
Xinchao Song1, Mingjun Li1, Sean Banerjee2
1Clarkson University, Department of Computer Science, 8 Clarkson Avenue, Potsdam, NY 13699, USA.
Data in brief
|February 2, 2026
概括
HILO数据集为人工智能和机器人研究提供高分辨率的3D对象模型和多视图RGB-D图像. 它使训练算法能够执行诸如对象识别和机器人掌握计划等任务.
科学领域:
- 计算机视觉 计算机视觉
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
背景情况:
- 开发强大的AI和机器人算法需要具有多样化的对象表示和多视图成像的全面数据集.
- 现有的数据集往往缺乏高分辨率的3D模型和高级任务所需的精确姿势信息.
研究的目的:
- 介绍HILO数据集,这是一个用于推进计算机视觉,人工智能和机器人的新型资源.
- 为常见物体和桌面场景提供高分辨率的3D扫描模型和多视图RGB-D图像.
- 促进对象识别,细分和机器人掌握规划算法的培训和评估.
主要方法:
- 使用桌面3D扫描仪获取253个常见对象的超高分辨率3D模型.
- 通过使用带有Azure Kinect传感器的机器人测试台,捕获了144个桌面场景的32,256张多视图RGB-D图像.
- 执行模型与图像的精确3D对齐,以获得6-DOF姿势和提取对象元数据.
主要成果:
- HILO数据集包括253个高分辨率3D对象模型和32,256个多视图RGB-D图像.
- 为场景中的对象提供精确的6-DOF姿势信息.
- 包括丰富的元数据,如对象质量,类别和尺寸比.
结论:
- HILO数据集是开发和基准测试人工智能和机器人算法的宝贵资源.
- 能够在包括对象完成,识别,细分和掌握计划在内的领域进行研究.
- 支持通过识别人类喜欢的抓取位置来推进人机协作.
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