# Submodular Trajectory Optimization for Aerial 3D Scanning

arXiv: https://arxiv.org/abs/1705.00703 · HTML: https://mlsanity.com/paper/1705.00703/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 May 2017.

## Built on by (most cited first, 16 of 16)

- [Text2Room: Extracting Textured 3D Meshes from 2D Text-to-Image Models](https://mlsanity.com/paper/2303.11989/) (2023)
- [Capturing, Reconstructing, and Simulating: the UrbanScene3D Dataset](https://mlsanity.com/paper/2107.04286/) (2021)
- [A Review on Viewpoints and Path-planning for UAV-based 3D Reconstruction](https://mlsanity.com/paper/2205.03716/) (2022)
- [Learning Reconstructability for Drone Aerial Path Planning](https://mlsanity.com/paper/2209.10174/) (2022)
- [Optimizing the Placement of Roadside LiDARs for Autonomous Driving](https://mlsanity.com/paper/2310.07247/) (2023)
- [Resource-Aware Distributed Submodular Maximization: A Paradigm for Multi-Robot Decision-Making](https://mlsanity.com/paper/2204.07520/) (2022)
- [MAGICIAN: Efficient Long-Term Planning with Imagined Gaussians for Active Mapping](https://mlsanity.com/paper/2603.22650/) (2026)
- [Greedy Perspectives: Multi-Drone View Planning for Collaborative Perception in Cluttered Environments](https://mlsanity.com/paper/2310.10863/) (2023)
- [Holistic Inverse Rendering of Complex Facade via Aerial 3D Scanning](https://mlsanity.com/paper/2311.11825/) (2023)
- [VIN-NBV: A View Introspection Network for Next-Best-View Selection](https://mlsanity.com/paper/2505.06219/) (2025)
- [ACT-R: Adaptive Camera Trajectories for Single View 3D Reconstruction](https://mlsanity.com/paper/2505.08239/) (2025)
- [IOVS4NeRF:Incremental Optimal View Selection for Large-Scale NeRFs](https://mlsanity.com/paper/2407.18611/) (2024)
- [Camera Splatting for Continuous View Optimization](https://mlsanity.com/paper/2509.15677/) (2025)
- [HERE: Hierarchical Active Exploration of Radiance Field with Epistemic Uncertainty Minimization](https://mlsanity.com/paper/2601.07242/) (2026)
- [Iterative Hybrid Discrete-Continuous Viewpoint Planning for UAV Photogrammetry](https://mlsanity.com/paper/2608.05718/) (2026)
- [DA-NBV: A Direction-Aware Next-Best-View Planner for Efficient 3D Reconstruction of Ships at Sea](https://mlsanity.com/paper/2608.08025/) (2026)

## Abstract (arXiv)

Drones equipped with cameras are emerging as a powerful tool for large-scale aerial 3D scanning, but existing automatic flight planners do not exploit all available information about the scene, and can therefore produce inaccurate and incomplete 3D models. We present an automatic method to generate drone trajectories, such that the imagery acquired during the flight will later produce a high-fidelity 3D model. Our method uses a coarse estimate of the scene geometry to plan camera trajectories that: (1) cover the scene as thoroughly as possible; (2) encourage observations of scene geometry from a diverse set of viewing angles; (3) avoid obstacles; and (4) respect a user-specified flight time budget. Our method relies on a mathematical model of scene coverage that exhibits an intuitive diminishing returns property known as submodularity. We leverage this property extensively to design a trajectory planning algorithm that reasons globally about the non-additive coverage reward obtained across a trajectory, jointly with the cost of traveling between views. We evaluate our method by using it to scan three large outdoor scenes, and we perform a quantitative evaluation using a photorealistic video game simulator.