DroneDeploy Aerial vs OpenMVG
DroneDeploy Aerial
dronedeploy.comOpenMVG
github.comSide by side
| Figure | DroneDeploy Aerial | OpenMVG |
|---|---|---|
| Price from | $159/mo | Free |
| Free plan | — | Yes |
| Free trial | Yes | — |
| Open source | — | Yes |
| Web | Yes | — |
| Windows | — | Yes |
| macOS | — | Yes |
| Linux | — | Yes |
| iOS | Yes | Yes |
| Android | Yes | Yes |
| API | Yes | Yes |
| Self-hosted | — | Yes |
| Plans published | 4 | 1 |
| Spec score · Photogrammetry Software | 8.3 (#11) | 8.6 (#4) |
| Purpose | DroneDeploy Aerial lets teams fly drones, capture site data and turn it into automated insights. | OpenMVG is a multiple view geometry library for 3D computer vision and structure from motion. |
| Support | The Flight & Analysis plan includes email and live chat support; Ag Lite lists email-based support. | The repository lists a project contact email and directs users to its wiki for tutorials and usage resources. |
Plans
DroneDeploy Aerial
- Ag Lite Plan$1,908 a year
- Flight & Analysis Plan$4,188 a year
- Advanced Flight & Analysis PlanPriced on request
- DroneDeploy AerialPriced on request
In their own words
DroneDeploy Aerial
DroneDeploy Aerial has no write-up on Specifiction yet; its figures are above.
OpenMVG
OpenMVG is an open-source C++ library for multiple-view geometry, 3D computer vision, and structure from motion. It provides libraries, binaries, and pipelines for reconstructing 3D scenes from images. Library areas include image manipulation, feature description and matching, feature tracking, camera models, multiple-view geometry, robust estimation, and structure-from-motion algorithms. Its binaries can initialize scenes, detect and match features, perform reconstruction, and export scenes to other multiple-view-stereo frameworks. Those frameworks can compute dense point clouds or textured meshes from exported scenes. OpenMVG is released under the Mozilla Public License 2.0 and is listed as free. The project says it aims to support reproducible research through readable implementations of classic and state-of-the-art algorithms. It is designed for reading, learning, modification, and use, with test-driven development and samples. Tutorials cover data structures, library use, and processing an image dataset. Platforms listed are Windows, macOS, Linux, iOS, Android, and self-hosted, with desktop deployment, API access, point-cloud export, and ground control point support.