DepthNet training on Still Box
Project page
This code can replicate the results of our paper that was published in UAVg-17. If you use this repo in your work, please cite us with the following bibtex :
@Article{isprs-annals-IV-2-W3-67-2017,
AUTHOR = {Pinard, C. and Chevalley, L. and Manzanera, A. and Filliat, D.},
TITLE = {END-TO-END DEPTH FROM MOTION WITH STABILIZED MONOCULAR VIDEOS},
JOURNAL = {ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences},
VOLUME = {IV-2/W3},
YEAR = {2017},
PAGES = {67--74},
URL = {https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/IV-2-W3/67/2017/},
DOI = {10.5194/isprs-annals-IV-2-W3-67-2017}
}
End-to-end depth from motion with stabilized monocular videos
- This code shows how the only translational movement of the camera can be leveraged to compute a very precise depth map, even at more than 300 times the displacement.
- Thus, for a camera movement of 30cm (nominal displacement used here), you can see as far as 100m.
See our second paper for information about using this code on real videos with speed estimation
Click Below for video
DepthNet
DepthNet is a network designed to infer Depth Map directly from a pair of stabilized image.
- No information is given about movement direction
- DepthNet is Fully Convolutional, which means it is completely robust to optical center fault
- This network only works for pinhole-like pictures
Still Box
Still box is a dataset created specifically for supervised training of depth map inference for stabilized aerial footage. It tries to mimic typical drone footages in static scenes, and depth is impossible to infer from a single image, as shapes get all kinds of sizes and positions.
- You can download it here
- The dataset webpage also provides a tutorial on how to read the data
Training
Requirements
[sudo] pip3 install -r requirements.txt
If you want to log some outputs from the validation set with the --log-output option, you need openCV python bindings to convert depth to RGB with a rainbow colormap.
If you don't have opencv, grayscales will be logged
Usage
Best results can be obtained by training on still box 64 and then finetuned successively up to the resolution you target. Here are the parameters used for the paper (please note how learning rate and batch size are changed, training was done a single GTX 980Ti).
python3 train.py -j8 --lr 0.01 /path/to/still_box/64/ --log-output --activation-function elu --bn
python3 train.py -j8 --lr 0.01 /path/to/still_box/128/ --log-output --activation-function elu --bn --pretrained /path/to/DepthNet64
python3 train.py -j8 --lr 0.001 /path/to/still_box/256/ --log-output --activation-function elu --bn -b64 --pretrained /path/to/DepthNet128
python3 train.py -j8 --lr 0.001 /path/to/still_box/512/ --log-output --activation-function elu --bn -b16 --pretrained /path/to/DepthNet256
Note: You can skip 128 and 256 training if you don't have time, results will be only slightly worse. However, you need to do 64 training first as stated by our first paper. This might has something to do with either the size of 64 dataset (in terms of scene numbers) or the fact that feature maps are reduced down to 1x1 making last convolution a FC equivalent operation
Pretrained networks
Best results were obtained with elu for depth activation (not mentionned in the original paper), along with BatchNorm.
| Name | training set | Error (m) | |
|---|---|---|---|
DepthNet_elu_bn_64.pth.tar |
64 | 4.65 | Link |
DepthNet_elu_bn_128.pth.tar |
128 | 3.08 | Link |
DepthNet_elu_bn_256.pth.tar |
256 | 2.29 | Link |
DepthNet_elu_bn_512.pth.tar |
512 | 1.97 | Link |
All the networks have the same size and same structure.
Custom FOV and focal length
Every image in still box is 90° of FOV (field of view), focal length (in pixels) is then respectively
- 32px for 64x64 images
- 64px for 128x128 images
- 128px for 128x128 images
- 256px for 512x512 images
Training is not flexible to focal length, and for a custom focal length you will have to run a dedicated training.
If you need to use a custom focal length and FOV you can simply resize the pictures and crop them.
Say you have a picture of width w with an associated FOV fov. To get equivalent from one of the datasets you can first crop the still box pictures so that FOV will match fov (cropping doesn't affect focal length in pixels), and then resize it to w. Note that DepthNet can take rectangular pictures as input.
cropped_w = w/tan(pi*fov/360)
we naturally recommend to do this operation offline, metadata from metadata.json won't need to be altered.
with pretrained DepthNet
If you can resize your test pictures, thanks to its fully convolutional architecture, DepthNet is flexible to fov, as long as it stays below 90° (or max FOV encountered during training). Referring back to our witdh w and FOV fov we get with a network trained with a particular focal length f the following width to resize to:
resized_w = f/2*tan(pi*fov/360)
That way, you won't have to make a dedicated training or even download the still box dataset
/!\ These equations are only valid with pinhole equivalent cameras. Be sure to correct distortion before using DepthNet
Testing Inference
The run_inference.py lets you run an inference on a folder of images, and save the depth maps in different visualizations.
A simple still box scene of 512x512 pictures for testing can be downloaded here. Otherwise, any folder with a list of jpg images will do, provided you follow the guidelines above.
python3 run_inference.py --output-depth --no-resize --dataset-dir /path/to/stub_box --pretrained /path/to/DepthNet512 --frame-shift 3 --output-dir /path/to/save/outputs
Visualise training
Training can be visualized via tensorboard by launching this command in another terminal
tensorboard --logdir=/path/to/DepthNet/Results
You can then access the board from any computer in the local network by accessing machine_ip:6006 from a web browser, just as a regular tensorboard server. More info here
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