多无人机协同捕捉户外多人场景，解决视角冲突与遮挡问题。
CoCap: Coordinated motion Capture for multi-actor scenes in outdoor environments
- 基于冲突搜索思想，协调多机视角规划以保证跨相机一致性。
- 在高遮挡环境下性能逼近无约束理想方案，优于传统顺序规划方法。
- 提供单机实时搜索策略，适合密集环境下的快速响应应用。
动作捕捉在计算机动画、虚拟现实、生物信息学和人形机器人训练中日益重要。户外环境虽扩展了视野范围，但带来遮挡和障碍物挑战。现有基于多无人机系统的多人场景捕捉方法常忽略多视角一致性及复杂环境中的跨相机推理。受冲突基础搜索（CBS）启发，本文提出协同动作捕捉（CoCap），通过协调视角规划确保冲突情况下的多视角推理。在高遮挡与障碍物场景下，机器人间碰撞概率上升时，CoCap性能接近无约束规划的理想结果，显著优于现有顺序规划方法。此外，还提出适用于密集环境的单机视角搜索策略，支持实时应用。
原文摘要 · Abstract (English)
Motion capture has become increasingly important, not only in computer animation but also in emerging fields like the virtual reality, bioinformatics, and humanoid training. Capturing outdoor environments offers extended horizon scenes but introduces challenges with occlusions and obstacles. Recent approaches using multi-drone systems to capture multiple actor scenes often fail to account for multi-view consistency and reasoning across cameras in cluttered environments. Coordinated motion Capture (CoCap), inspired by Conflict-Based Search (CBS), addresses this issue by coordinating view planning to ensure multi-view reasoning during conflicts. In scenarios with high occlusions and obstacles, where the likelihood of inter-robot collisions increases, CoCap demonstrates performance that approaches the ideal outcomes of unconstrained planning, outperforming existing sequential planning methods. Additionally, CoCap offers a single-robot view search approach for real-time applications in dense environments.
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