@@ -8,8 +8,8 @@ For a brief recap of what it does, see section [How it works](#how-it-works)
*[Software Dependencies](#software-dependencies)
*[Hardware Dependencies](#hardware-dependencies)
*[How it works](#how-it-works)
*[Step by step guide](#usage)
*[Special case : adding new images to an existing constructed dataset](#special-case-adding-new-images-to-an-existing-dataset)
*[Usage](#usage)
*[Special case : adding new images to an existing constructed dataset](#special-case--adding-new-images-to-an-existing-dataset)
*[Using the constructed dataset for evaluation](#evaluation)
*[Detailed method with the manoir example](#detailed-method-with-the-manoir-example)
*[TODO](#todo)
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@@ -38,7 +38,7 @@ For PDrAW, there should be a `native-wrapper.sh` file that you to keep a track o
For COLMAP, you will need a vocab tree for feature matching. You can download them at https://demuc.de/colmap/#download . In our tests, we took the 256K version.
## Hardware dependecies
## Hardware dependencies
To recreate the results of the study, you will need these hardware pieces :
- Parrot Anafi
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@@ -1100,6 +1100,9 @@ All these steps can be done under the script `picture_localization.py` with the
*`--rebuild_occlusion_mesh` : If selected, will rebuild a new dense point cloud and delauney mesh. Useful when new images see new parts of the model
*`--generic_model` : COLMAP model for image folders. Same zoom level assumed throughout whole folders. See https://colmap.github.io/cameras.html (default: OPENCV)
## Evaluation
TODO
## Detailed method with the "Manoir" example
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@@ -1175,7 +1178,7 @@ Thorough photogrammetry was done with 1000 frames. Notice that not all the area
