Commit 26a2bfdf authored by Clément Pinard's avatar Clément Pinard
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parent b3288306
......@@ -10,7 +10,9 @@ For a brief recap of what it does, see section [How it works](#how-it-works)
* [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)
* [Using the constructed dataset for evaluation](#evaluation)
* [Detailed method with the manoir example](#detailed-method-with-the-manoir-example)
* [TODO](#todo)
## Software Dependencies
......@@ -733,6 +735,26 @@ This will essentially do the same thing as the script, in order to let you chang
--threads 8
This will create a dataset at the folder `/path/to/dataset/` with images, depth maps in npy format, camera intrinsics and distortion in txt and yaml, pose information in the same format as KITTI odometry, and relevant metadata stored in a csv file.
16. Evaluation list creation
Once everything is constructed, you can specify a subset of e.g. 500 frames for evaluaton.
python \
--dataset_dir /path/to/dataset/ \
--split 0.9 \
--seed 0 \
--min_shift 50 \
this will select 500 frames (at most) such that 90% (`--split 0.9`) of folders are kept as training folders, and every frame has at least 50 frames with valid odometry before (`--min_shift 50`). Interpolated frames are allowed for odometry to be considered valid (but not for depth ground truth) (`--allow_interpolated_frames`)
It will create a txt file with test file paths (`/path/to/dataset/test_files.txt`), a txt file with train folders (`/path/to/dataset/train_folders.txt`) and lastly a txt file with flight path vector coordinates (in pixels) (`/path/to/dataset/fpv.txt`)
### Special case : Adding new images to an existing dataset
In case you already have constructed a dataset and you still have the workspace that used available, you can easily add new images to the dataset. See
......@@ -834,4 +856,21 @@ Thorough photogrammetry was done with 1000 frames. Notice that not all the area
### Resulting video
[![Alt text](](
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[![Alt text](](
## Better point cloud registration
- See `` : add chamfer loss to regular bundle adjustment, so that the reconstruction not only minimizes pixel reprojection but also distance to Lidar Point Cloud
## Better filtering of models :
- for now we can only interpolate everything or nothing, add a threshold time above which we don't consider the pose interpolation reliable anymore, even for odometry
- (not sure if useful) add camera parmeters filtering and interpolation, could be used when smooth zoom is applied
## Dataset homogeneization
- Apply rectification on the whole dataset to only have pinhole cameras in the end
- Resize all frames to have the exact same width, height, and intrinsics for particular algorithm that are trained on a specific set of intrinsics (see DepthNet)
- Divide videos into sequential subparts so that each folder will contain subsequent frames with valid absolute pose and depth
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