Paparazzo: Active Mapping of Moving 3D Objects

2026-04-21

AI Summary

This work addresses the limitation of existing 3D mapping methods, which typically assume static environments and struggle to reconstruct moving objects effectively. We introduce, for the first time, an active mapping task specifically designed for mobile objects. Our approach leverages geometric reasoning and optimization—without relying on learning-based components—to achieve robust trajectory prediction. By selecting observation viewpoints that maximize information gain and dynamically planning the agent’s path accordingly, our method enables high-quality reconstruction of moving targets. We further present the first dedicated benchmark dataset for this task. Experimental results demonstrate that our approach significantly outperforms multiple strong baselines, achieving notable improvements in both reconstruction completeness and accuracy.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Computer Vision: Motion & Tracking
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

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/

Problem

• active mapping
• moving objects
• 3D reconstruction
• dynamic scenes
• trajectory planning

Innovation

• active mapping
• moving 3D objects
• trajectory prediction
• viewpoint planning
• dynamic scene reconstruction