# A Review on Viewpoints and Path-planning for UAV-based 3D Reconstruction

arXiv: https://arxiv.org/abs/2205.03716 · HTML: https://mlsanity.com/paper/2205.03716/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 37 of its 213 references that are in the map.
- First posted on arXiv 7 May 2022.

## Builds on (most cited first, 30 of 37)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [ORB-SLAM: a Versatile and Accurate Monocular SLAM System](https://mlsanity.com/paper/1502.00956/) (2015)
- [AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles](https://mlsanity.com/paper/1705.05065/) (2017)
- [Cascade Cost Volume for High-Resolution Multi-View Stereo and Stereo Matching](https://mlsanity.com/paper/1912.06378/) (2019)
- [Voxblox: Incremental 3D Euclidean Signed Distance Fields for On-Board MAV Planning](https://mlsanity.com/paper/1611.03631/) (2016)
- [Capturing, Reconstructing, and Simulating: the UrbanScene3D Dataset](https://mlsanity.com/paper/2107.04286/) (2021)
- [Ground-aware Monocular 3D Object Detection for Autonomous Driving](https://mlsanity.com/paper/2102.00690/) (2021)
- [An Efficient Sampling-based Method for Online Informative Path Planning in Unknown Environments](https://mlsanity.com/paper/1909.09548/) (2019)
- [Next-Best View Policy for 3D Reconstruction](https://mlsanity.com/paper/2008.12664/) (2020)
- [On the folded normal distribution](https://mlsanity.com/paper/1402.3559/) (2014)
- [Fast Frontier-based Information-driven Autonomous Exploration with an MAV](https://mlsanity.com/paper/2002.04440/) (2020)
- [Submodular Trajectory Optimization for Aerial 3D Scanning](https://mlsanity.com/paper/1705.00703/) (2017)
- [Learn-to-Score: Efficient 3D Scene Exploration by Predicting View Utility](https://mlsanity.com/paper/1806.10354/) (2018)
- [Where to Look Next: Learning Viewpoint Recommendations for Informative Trajectory Planning](https://mlsanity.com/paper/2203.02381/) (2022)
- [A Multi-Resolution Frontier-Based Planner for Autonomous 3D Exploration](https://mlsanity.com/paper/2011.02182/) (2020)
- [Online Informative Path Planning for Active Classification Using UAVs](https://mlsanity.com/paper/1609.08446/) (2016)
- [A Reinforcement Learning Approach to the View Planning Problem](https://mlsanity.com/paper/1610.06204/) (2016)
- [Viewpoint Planning for Fruit Size and Position Estimation](https://mlsanity.com/paper/2011.00275/) (2020)
- [A Shadowcasting-Based Next-Best-View Planner for Autonomous 3D Exploration](https://mlsanity.com/paper/2109.09323/) (2021)
- [Multi-UAV Coverage Path Planning for the Inspection of Large and Complex Structures](https://mlsanity.com/paper/2007.13065/) (2020)
- [Multi-UAV trajectory planning for 3D visual inspection of complex structures](https://mlsanity.com/paper/2204.10070/) (2022)
- [UAV-based Autonomous Image Acquisition with Multi-View Stereo Quality Assurance by Confidence Prediction](https://mlsanity.com/paper/1605.01923/) (2016)
- [Adaptive View Planning for Aerial 3D Reconstruction](https://mlsanity.com/paper/1805.00506/) (2018)
- [Do You See What I See? Coordinating Multiple Aerial Cameras for Robot Cinematography](https://mlsanity.com/paper/2011.05437/) (2020)
- [REAL: Rapid Exploration with Active Loop-Closing toward Large-Scale 3D Mapping using UAVs](https://mlsanity.com/paper/2108.02590/) (2021)
- [Coverage Path Planning using Path Primitive Sampling and Primitive Coverage Graph for Visual Inspection](https://mlsanity.com/paper/1908.02901/) (2019)

## Built on by (most cited first, 14 of 14)

- [GauSS-MI: Gaussian Splatting Shannon Mutual Information for Active 3D Reconstruction](https://mlsanity.com/paper/2504.21067/) (2025)
- [UAVs Meet Agentic AI: A Multidomain Survey of Autonomous Aerial Intelligence and Agentic UAVs](https://mlsanity.com/paper/2506.08045/) (2025)
- [Bag of Views: An Appearance-based Approach to Next-Best-View Planning for 3D Reconstruction](https://mlsanity.com/paper/2307.05832/) (2023)
- [A Review on Geometry and Surface Inspection in 3D Concrete Printing](https://mlsanity.com/paper/2503.07472/) (2025)
- [DM-OSVP++: One-Shot View Planning Using 3D Diffusion Models for Active RGB-Based Object Reconstruction](https://mlsanity.com/paper/2504.11674/) (2025)
- [STT-GS: Sample-Then-Transmit Edge Gaussian Splatting with Joint Client Selection and Power Control](https://mlsanity.com/paper/2510.13186/) (2025)
- [DiffPoint: Single and Multi-view Point Cloud Reconstruction with ViT Based Diffusion Model](https://mlsanity.com/paper/2402.11241/) (2024)
- [A New Clustering-based View Planning Method for Building Inspection with Drone](https://mlsanity.com/paper/2408.01435/) (2024)
- [UAVLight: A Benchmark for Illumination-Robust 3D Reconstruction in Unmanned Aerial Vehicle (UAV) Scenes](https://mlsanity.com/paper/2511.21565/) (2025)
- [On-the-fly Feedback SfM: Online Explore-and-Exploit UAV Photogrammetry with Incremental Mesh Quality-Aware Indicator and Predictive Path Planning](https://mlsanity.com/paper/2512.02375/) (2025)
- [Very High-Resolution Bridge Deformation Monitoring Using UAV-based Photogrammetry](https://mlsanity.com/paper/2410.18984/) (2024)
- [Neural 3D Object Reconstruction with Small-Scale Unmanned Aerial Vehicles](https://mlsanity.com/paper/2509.12458/) (2025)
- [RefGlass-GS: A UAV-Enabled Fusion Framework for Photorealistic, Semantic and Interactive Digitization of Reflective Glass Facades via Gaussian Splatting](https://mlsanity.com/paper/2606.28826/) (2026)
- [DAPM: UAV Monocular Depth Estimation from Any Height, Pitch, Roll and FOV](https://mlsanity.com/paper/2607.21438/) (2026)

## Abstract (arXiv)

Unmanned aerial vehicles (UAVs) are widely used platforms to carry data capturing sensors for various applications. The reason for this success can be found in many aspects: the high maneuverability of the UAVs, the capability of performing autonomous data acquisition, flying at different heights, and the possibility to reach almost any vantage point. The selection of appropriate viewpoints and planning the optimum trajectories of UAVs is an emerging topic that aims at increasing the automation, efficiency and reliability of the data capturing process to achieve a dataset with desired quality. On the other hand, 3D reconstruction using the data captured by UAVs is also attracting attention in research and industry. This review paper investigates a wide range of model-free and model-based algorithms for viewpoint and path planning for 3D reconstruction of large-scale objects. The analyzed approaches are limited to those that employ a single-UAV as a data capturing platform for outdoor 3D reconstruction purposes. In addition to discussing the evaluation strategies, this paper also highlights the innovations and limitations of the investigated approaches. It concludes with a critical analysis of the existing challenges and future research perspectives.