Paparazzo: Active Mapping of Moving 3D Objects
For robotics and 3D vision, this work addresses the limitation of static mapping pipelines by enabling active reconstruction of moving objects, though the method is learning-free and task-specific.
The paper introduces the task of active mapping of moving objects and presents Paparazzo, a learning-free method that plans camera trajectories to reconstruct moving objects. It achieves significant improvements in 3D reconstruction completeness and accuracy over baselines.
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/