Stealthy Coverage Control for Human-enabled Real-Time 3D Reconstruction

2026-01-31

AI Summary

This work addresses the challenge of efficiently acquiring images for high-quality real-time 3D reconstruction in the absence of prior structural knowledge about the target scene. The authors propose a semi-autonomous image sampling strategy that innovatively integrates human judgment—particularly in complex or ambiguous regions—with autonomous drone coverage control. A key contribution is the “stealth coverage control” mechanism, which decouples human-guided navigation from automated sampling to effectively prevent conflicts between manual and autonomous operations. Implemented within a Unity/ROS2 simulation framework, the system unifies human intent input, coverage control algorithms, and adaptive sampling logic. Experimental results demonstrate that the proposed approach significantly outperforms fully autonomous methods in reconstruction quality, achieving efficient and effective human–robot collaborative 3D reconstruction.

Technology Category

• Intelligent Robots: State Estimation
• Search and Optimization: Sampling/Simulation-based Search
• Humans and AI: Planning and Decision Support for Human-Machine Teams

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

In this paper, we propose a novel semi-autonomous image sampling strategy, called stealthy coverage control, for human-enabled 3D structure reconstruction. The present mission involves a fundamental problem: while the number of images required to accurately reconstruct a 3D model depends on the structural complexity of the target scene to be reconstructed, it is not realistic to assume prior knowledge of the spatially non-uniform structural complexity. We approach this issue by leveraging human flexible reasoning and situational recognition capabilities. Specifically, we design a semi-autonomous system that leaves identification of regions that need more images and navigation of the drones to such regions to a human operator. To this end, we first present a way to reflect the human intention in autonomous coverage control. Subsequently, in order to avoid operational conflicts between manual control and autonomous coverage control, we develop the stealthy coverage control that decouples the drone motion for efficient image sampling from navigation by the human. Simulation studies on a Unity/ROS2-based simulator demonstrate that the present semi-autonomous system outperforms the one without human interventions in the sense of the reconstructed model quality.

Problem

• 3D reconstruction
• coverage control
• structural complexity
• image sampling
• human-in-the-loop

Innovation

• stealthy coverage control
• semi-autonomous system
• human-in-the-loop
• 3D reconstruction
• drone navigation