Collaborative Research: NRI: INT: Dense 3D Reconstruction of Dynamic Actors in Natural Environments using Multiple Flying Cameras

合作研究:NRI:INT:使用多个飞行摄像机对自然环境中的动态演员进行密集 3D 重建

基本信息

  • 批准号:
    2022894
  • 负责人:
  • 金额:
    $ 63.85万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2020
  • 资助国家:
    美国
  • 起止时间:
    2020-10-01 至 2024-09-30
  • 项目状态:
    已结题

项目摘要

While large-scale multi-camera domes have been developed for data collection in controlled laboratory settings it is not possible to achieve a similar level of measurement quality outdoors where there is much potential benefit to such data collection. For example, use of such measurements include the body dynamics of a running cheetah, or people, or analyzing herding behaviors of animals or birds. This leads to scientists relying on extremely inefficient and dangerous data collection methods. For example, biologists studying the behaviors of wild animals try to predict where the animals will be and place some cameras which only give some limited data at specific locations. This project addresses such challenges by exploring the research of methods and development of a large-scale data collection tool for high-resolution and multi-viewpoint visual recording and motion analysis of natural group behaviors (e.g., herds of animals or groups of people) in-the-wild over very large environments (e.g., desert plains or mountain sides) using a team of flying robots. This project develops computational models that integrate the fundamentals of computer vision and multi-agent control to measure the group of actors in 3D. Through the development of this system, this project will make major advances in technology at the intersection of perception and control that include: (1) a new study of methods for precise, rapid, and robust target motion forecasting and relative state estimation that estimates the 3D motion of the robots and actors quickly with strong uncertainty estimates; (2) a new decomposition of the perception-aware multi-objective multi-UAV safe motion planning problem, that allows long-term planning based on consistent actor forecasting uncertainty models and coverage objectives; (3) a new guaranteed safe but adaptive paradigm for reactive flight control that is able to generate safety maneuvers even under large disturbances and vehicle dynamics changes, and that can leverage prior flight experience for real-time adaptation; (4) new theory of 3D reconstruction for dynamic scenes captured by UAVs that will enable high-resolution mesh and skeletal reconstruction of the groups of actors. The research outcome will be disseminated through multiple educational activities.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
虽然已经开发了用于在受控实验室环境中进行数据收集的大规模多摄像头圆顶,但是在户外不可能实现类似水平的测量质量,而户外对这种数据收集有很大的潜在益处。例如,这些测量的使用包括奔跑的猎豹或人的身体动态,或分析动物或鸟类的放牧行为。这导致科学家依赖于极其低效和危险的数据收集方法。例如,研究野生动物行为的生物学家试图预测动物的位置,并在特定位置放置一些只能提供有限数据的摄像机。本项目通过探索方法的研究和大规模数据收集工具的开发来应对这些挑战,该工具用于高分辨率和多视点的视觉记录和自然群体行为的运动分析(例如,动物群或人群)在非常大的环境(例如,沙漠平原或山腰)使用一组飞行机器人。该项目开发的计算模型集成了计算机视觉和多智能体控制的基础知识,以测量3D中的演员组。通过该系统的开发,本项目将在感知和控制的交叉点上取得重大进展,包括:(1)对精确、快速和鲁棒的目标运动预测和相对状态估计方法的新研究,该方法快速估计机器人和演员的3D运动,具有很强的不确定性估计;(2)对感知感知的多目标多无人机安全运动规划问题进行了新的分解,允许基于一致的行动者预测不确定性模型和覆盖目标进行长期规划;(3)一种新的有保证的安全但自适应的反应式飞行控制范例,即使在大的干扰和飞行器动态变化的情况下也能够产生安全机动,并且能够利用先前的飞行经验进行实时自适应;(4)无人机动态场景三维重建的新理论,实现了多组角色的高分辨率网格和骨架重建。该奖项反映了NSF的法定使命,并通过使用基金会的知识价值和更广泛的影响审查标准进行评估,被认为值得支持。

项目成果

期刊论文数量(3)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Learning to Play Pursuit-Evasion with Visibility Constraints
Normal-guided Garment UV Prediction for Human Re-texturing
Self-supervised 3D Representation Learning of Dressed Humans from Social Media Videos
从社交媒体视频中对着装人类进行自监督 3D 表示学习
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Hyun Soo Park其他文献

872: A comparison of Ritodrine and Magnesium sulfate for preterm labor: a randomized clinical trial
  • DOI:
    10.1016/j.ajog.2019.11.885
  • 发表时间:
    2020-01-01
  • 期刊:
  • 影响因子:
  • 作者:
    Young Mi Jung;Seung Mi Lee;Sun Min Kim;Byoung Jae Kim;Seokyung Han;Jeong Woo Park;Hyun Soo Park;Kyung A. Lee;Chan-Wook Park;Jong Kwan Jun;Joong Shin Park
  • 通讯作者:
    Joong Shin Park
An Extended Workspace Mapping Algorithm and its Implementation in a Nuclear Tele-Robotic Control System
  • DOI:
    10.1016/s1474-6670(17)49861-2
  • 发表时间:
    1992-09-01
  • 期刊:
  • 影响因子:
  • 作者:
    Young Soo Park;Woo Tae Jeong;Ji Sup Yoon;Jae Sol Lee;Hyun Soo Park;Hyung Suck Cho
  • 通讯作者:
    Hyung Suck Cho
Absorption of NOx in packed column (II)
  • DOI:
    10.1007/bf02697336
  • 发表时间:
    1990-01-01
  • 期刊:
  • 影响因子:
    3.200
  • 作者:
    Hoo Kun Lee;Myeong Soo Jeong;Joo Wan Park;Hyun Soo Park;Jong Hyun Cho
  • 通讯作者:
    Jong Hyun Cho
Motion-Based Temporal Alignment of Independently Moving Cameras
独立移动相机基于运动的时间对齐
3D-printed multifunctional materials enabled by artificial-intelligence-assisted fabrication technologies
通过人工智能辅助制造技术实现的 3D 打印多功能材料
  • DOI:
    10.1038/s41578-020-00235-2
  • 发表时间:
    2020-10-12
  • 期刊:
  • 影响因子:
    86.200
  • 作者:
    Zhijie Zhu;Daniel Wai Hou Ng;Hyun Soo Park;Michael C. McAlpine
  • 通讯作者:
    Michael C. McAlpine

Hyun Soo Park的其他文献

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{{ truncateString('Hyun Soo Park', 18)}}的其他基金

RI: Small: Learning 3D Equivariant Visual Representation for Animals
RI:小:学习动物的 3D 等变视觉表示
  • 批准号:
    2202024
  • 财政年份:
    2022
  • 资助金额:
    $ 63.85万
  • 项目类别:
    Standard Grant
NCS-FO: Neural Correlates of Social States in Macaques
NCS-FO:猕猴社会状态的神经相关性
  • 批准号:
    2024581
  • 财政年份:
    2020
  • 资助金额:
    $ 63.85万
  • 项目类别:
    Standard Grant
MRI: Development of Real-time 3D Social Signal Imaging System (SSIS)
MRI:实时 3D 社交信号成像系统 (SSIS) 的开发
  • 批准号:
    1919965
  • 财政年份:
    2019
  • 资助金额:
    $ 63.85万
  • 项目类别:
    Standard Grant
CAREER: Raster Multiview Algebra for Unlabeled Visual Data Exploration
职业:用于无标签视觉数据探索的栅格多视图代数
  • 批准号:
    1846031
  • 财政年份:
    2019
  • 资助金额:
    $ 63.85万
  • 项目类别:
    Continuing Grant
CRII: RI: Towards Learning Skills from First Person Demonstrations
CRII:RI:从第一人称演示中学习技能
  • 批准号:
    1755895
  • 财政年份:
    2018
  • 资助金额:
    $ 63.85万
  • 项目类别:
    Standard Grant
NRI: Large: Collaborative Research: Human-robot Coordinated Manipulation and Transportation of Large Objects
NRI:大型:协作研究:大型物体的人机协调操纵和运输
  • 批准号:
    1328722
  • 财政年份:
    2013
  • 资助金额:
    $ 63.85万
  • 项目类别:
    Continuing Grant

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