让机器人主动追踪移动物体，实现精准三维重建
Paparazzo: Active Mapping of Moving 3D Objects
- 不依赖学习，通过轨迹预测与视角优化规划路径
- 相比基线方法，重建完整度和准确率显著提升
- 适合需要动态场景建模的机器人与自动驾驶应用
当前3D映射流程普遍假设环境静态，难以准确捕捉和重建移动物体。为此，我们提出全新的主动映射移动物体任务：映射代理需在补偿物体运动的同时规划自身轨迹。我们的方法Paparazzo提供无学习解决方案，可鲁棒预测目标轨迹，并识别最具信息量的观测视角以规划路径。我们还构建了一个全面的基准测试集。大量实验表明，Paparazzo显著提升了3D重建的完整度与准确率，为动态场景理解迈出关键一步。
原文摘要 · Abstract (English)
Current 3D mapping pipelines generally assume static environments, which limits their ability to accurately capture and reconstruct moving objects. To address this limitation, we introduce the novel task of active mapping of moving objects, in which a mapping agent must plan its trajectory while compensating for the object's motion. Our approach, Paparazzo, provides a learning-free solution that robustly predicts the target's trajectory and identifies the most informative viewpoints from which to observe it, to plan its own path. We also contribute a comprehensive benchmark designed for this new task. Through extensive experiments, we show that Paparazzo significantly improves 3D reconstruction completeness and accuracy compared to several strong baselines, marking an important step toward dynamic scene understanding. Project page: https://davidea97.github.io/paparazzo-page/
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