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

May 07, 2022 ยท The Cartographer ยท ๐Ÿ› IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

๐Ÿ“š THE CARTOGRAPHER: The Cartographer
Survey/review paper โ€” maps the landscape rather than implementing a method.

"No code URL or promise found in abstract"
"Title-pattern auto-detect: A Review on Viewpoints and Path-planning for UAV-based 3D Reconstruction"

Evidence collected by the PWNC Scanner

Authors Mehdi Maboudi, MohammadReza Homaei, Soohwan Song, Shirin Malihi, Mohammad Saadatseresht, Markus Gerke arXiv ID 2205.03716 Category cs.CV: Computer Vision Cross-listed cs.GR, cs.RO Citations 112 Venue IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Last Checked 1 day ago
Abstract
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.
Community shame:
Not yet rated
Community Contributions

Found the code? Know the venue? Think something is wrong? Let us know!

๐Ÿ“œ Similar Papers

In the same crypt โ€” Computer Vision

๐ŸŒ… ๐ŸŒ… Old Age

Fast R-CNN

Ross Girshick

cs.CV ๐Ÿ› ICCV ๐Ÿ“š 27.7K cites 11 years ago