Paparazzo：运动三维目标的主动建图
Paparazzo: Active Mapping of Moving 3D Objects
把「主动视角规划」引入动态目标的三维重建：用无学习的方式预测目标轨迹并挑选信息量最大的观测视角，可直接用于无人机/机器人对运动物体的在线建图与扫描。
原文依据 a mapping agent must plan its trajectory while compensating for the object's motion
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论当前的三维建图流程通常假设环境是静态的，这限制了其准确捕捉与重建运动目标的能力。为弥补这一局限，我们提出了一个新任务：运动目标的主动建图，其中建图智能体必须在补偿目标运动的同时规划自身轨迹。我们的方法 Paparazzo 提供了一种无需学习的方案，能够稳健地预测目标轨迹并识别出观测该目标信息量最大的视角，从而规划自身路径。我们还为该新任务构建了一个全面的基准。通过大量实验，我们表明 Paparazzo 相比若干强基线显著提升了三维重建的完整性与准确性，朝着动态场景理解迈出了重要一步。项目主页：https://davidea97.github.io/paparazzo-page/
英文原文摘要 887 字符
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/