# Aerial Path Planning for Urban Geometry and Texture Co-Capture

arXiv: https://arxiv.org/abs/2509.22227 · HTML: https://mlsanity.com/paper/2509.22227/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 62 references that are in the map.
- First posted on arXiv 26 Sep 2025.

## Builds on (most cited first, 16 of 16)

- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [Instant Neural Graphics Primitives with a Multiresolution Hash Encoding](https://mlsanity.com/paper/2201.05989/) (2022)
- [Pyramid Scene Parsing Network](https://mlsanity.com/paper/1612.01105/) (2016)
- [2D Gaussian Splatting for Geometrically Accurate Radiance Fields](https://mlsanity.com/paper/2403.17888/) (2024)
- [VastGaussian: Vast 3D Gaussians for Large Scene Reconstruction](https://mlsanity.com/paper/2402.17427/) (2024)
- [RGBleftrightarrowX: Image decomposition and synthesis using material- and lighting-aware diffusion models](https://mlsanity.com/paper/2405.00666/) (2024)
- [An Efficient Sampling-based Method for Online Informative Path Planning in Unknown Environments](https://mlsanity.com/paper/1909.09548/) (2019)
- [Intrinsic3D: High-Quality 3D Reconstruction by Joint Appearance and Geometry Optimization with Spatially-Varying Lighting](https://mlsanity.com/paper/1708.01670/) (2017)
- [Fast Frontier-based Information-driven Autonomous Exploration with an MAV](https://mlsanity.com/paper/2002.04440/) (2020)
- [A Multi-Resolution Frontier-Based Planner for Autonomous 3D Exploration](https://mlsanity.com/paper/2011.02182/) (2020)
- [A Shadowcasting-Based Next-Best-View Planner for Autonomous 3D Exploration](https://mlsanity.com/paper/2109.09323/) (2021)
- [Learning Reconstructability for Drone Aerial Path Planning](https://mlsanity.com/paper/2209.10174/) (2022)
- [Plane-Based Optimization of Geometry and Texture for RGB-D Reconstruction of Indoor Scenes](https://mlsanity.com/paper/1905.09829/) (2019)
- [TwinTex: Geometry-aware Texture Generation for Abstracted 3D Architectural Models](https://mlsanity.com/paper/2309.11258/) (2023)
- [Projective Urban Texturing](https://mlsanity.com/paper/2201.10938/) (2022)

## Built on by (most cited first, 1 of 1)

- [A Bayesian Approach for Task-Specific Next-Best-View Selection with Uncertain Geometry](https://mlsanity.com/paper/2605.05095/) (2026)

## Abstract (arXiv)

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.