OptimalCap:在自由环境中高效和强大的基于LiDAR的动作捕捉
概括
OptimalCap为真实世界的场景提供了基于LiDAR的强大的人类运动捕捉. 这个框架实现了准确的,实时的多人跟踪,即使在杂的环境中,设置了一个新的标准.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 计算机视觉 计算机视觉
- 生物机械工程 生物机械工程
背景情况:
- 基于LiDAR的人类运动捕捉对于大规模,不受约束的设置是有希望的.
- 现有的方法与杂的数据和动态场景作斗争,限制了实际使用.
- 需要强大而高效的激光雷达运动捕捉解决方案.
研究的目的:
- 开发一个强大而高效的基于LiDAR的框架,用于准确的,实时的多人运动捕获.
- 引入一个大规模的合成数据集,用于训练和评估在噪音环境中的运动捕捉.
- 为了建立一个新的标准,可扩展的,现实世界的基于激光雷达的动作捕捉.
主要方法:
- 拟议的OptimalCap框架整合了层次结构骨架建模.
- 采用动态意识的时间优化,以实现连贯的运动捕捉.
- 介绍了NoiseMotion,一个合成数据集,模拟在噪音条件下的人物对象交互.
主要成果:
- OptimalCap实现了最先进的准确性,稳定性和时间一致性.
- 该框架支持超过20个同时被捕的个人.
- 在60 FPS的实时性能,达到100米的范围.
结论:
- OptimalCap在基于LiDAR的人类运动捕捉方面取得了重大进展.
- 该框架在具有挑战性,杂和动态的环境中非常有效.
- OptimalCap为可扩展和实用的现实世界运动捕捉应用程序设定了一个新的基准.
相关概念视频
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