Aerial Path Planning for Urban Geometry and Texture Co-Capture

2025-09-26

AI Summary

In urban 3D reconstruction, jointly optimizing geometric fidelity and texture quality remains challenging due to conflicting acquisition requirements. Method: This paper proposes an aerial path planning framework requiring only building footprints and a safe flight altitude. We formally define the geometry-texture co-acquisition problem for urban scenes and establish a facade-oriented texture quality assessment metric. Our approach employs a multi-view imaging strategy integrating vertical, oblique, and nadir views, coupled with a sequential path planning algorithm that jointly optimizes multiple objectives—including coverage, viewing angle, and occlusion—under explicit texture consistency constraints. Results: Experiments on large-scale synthetic and real-world urban datasets demonstrate that our method preserves high geometric accuracy while significantly improving texture fidelity, effectively suppressing visual artifacts (e.g., blurring, seams, and illumination inconsistencies), and reducing aerial data acquisition cost by over 30%.

Technology Category

• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Search and Optimization: Sampling/Simulation-based Search
• Constraint Satisfaction and Optimization: Constraint Learning and Acquisition

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Recent advances in image acquisition and scene reconstruction have enabled the generation of high-quality structural urban scene geometry, given sufficient site information. However, current capture techniques often overlook the crucial importance of texture quality, resulting in noticeable visual artifacts in the textured models. In this work, we introduce the urban geometry and texture co-capture problem under limited prior knowledge before a site visit. The only inputs are a 2D building contour map of the target area and a safe flying altitude above the buildings. We propose an innovative aerial path planning framework designed to co-capture images for reconstructing both structured geometry and high-fidelity textures. To evaluate and guide view planning, we introduce a comprehensive texture quality assessment system, including two novel metrics tailored for building facades. Firstly, our method generates high-quality vertical dipping views and horizontal planar views to effectively capture both geometric and textural details. A multi-objective optimization strategy is then proposed to jointly maximize texture fidelity, improve geometric accuracy, and minimize the cost associated with aerial views. Furthermore, we present a sequential path planning algorithm that accounts for texture consistency during image capture. Extensive experiments on large-scale synthetic and real-world urban datasets demonstrate that our approach effectively produces image sets suitable for concurrent geometric and texture reconstruction, enabling the creation of realistic, textured scene proxies at low operational cost.

Problem

• Co-capturing urban geometry and texture with limited prior knowledge
• Optimizing aerial path planning for high-fidelity texture reconstruction
• Developing texture quality metrics for building facade assessment

Innovation

• Generates vertical and horizontal views for detail capture
• Uses multi-objective optimization for texture and geometry
• Implements sequential path planning for texture consistency