Jamie Watson, Michael Firman, Aron Monszpart and Gabriel J. Brostow – CVPR 2020 (Oral presentation)
We introduce Footprints, a method for estimating the visible and hidden traversable space from a single RGB image
Understanding the shape of a scene from a single color image is a formidable computer vision task. Most methods aim to predict the geometry of surfaces that are visible to the camera, which is of limited use when planning paths for robots or augmented reality agents. Models which predict beyond the line of sight often parameterize the scene with voxels or meshes, which can be expensive to use in machine learning frameworks.
Our method predicts the hidden ground geometry and extent from a single image:
Our predictions enable virtual characters to more realistically explore their environment.
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| Baseline: The virtual character can only explore the ground visible to the camera | Ours: The penguin can explore both the visible and hidden ground |
Our code and models were developed with PyTorch 1.3.1.
The environment.yml and requirements.txt list our dependencies.
We recommend installing and activating a new conda environment from these files with:
conda env create -f environment.yml -n footprints
conda activate footprintsWe provide three pretrained models:
kitti, a model trained on the KITTI driving dataset with a resolution of 192x640,matterport, a model trained on the indoor Matterport dataset with a resolution of 512x640, andhandheld, a model trained on our own handheld stereo footage with a resolution of 256x448.
We provide code to make predictions for a single image, or a whole folder of images, using any of these pretrained models. Models will be automatically downloaded when required, and input images will be automatically resized to the correct input resolution for each model.
Single image prediction:
python -m footprints.predict_simple --image test_data/cyclist.jpg --model kittiMulti image prediction:
python -m footprints.predict_simple --image test_data --model handheldBy default, .npy predictions and .jpg visualisations will be saved to the predictions folder; this can be changed with the --save_dir flag.
To train a model you will need to download raw KITTI
and Matterport data. Edit the dataset field in paths.yaml to point to
the downloaded raw data paths.
For details on downloading KITTI, see Monodepth2.
You will also need per-image training data generated from the video sequences:
- visible ground segmentations
- hidden ground depths
- depth maps
- etc.
Our versions of these can be found HERE.
Download these and edit the training_data field of paths.yaml to point to them.
- KITTI
- Matterport
After this your paths.yaml should look like:
# Contents of paths.yaml
kitti:
dataset: <your_raw_KITTI_path>
training_data: <downloaded_KITTI_training_data>
matterport:
dataset: <your_raw_matterport_path>
training_data: <downloaded_matterport_training_data>
...
Now you have everything you need to train!
Train a KITTI model using:
CUDA_VISIBLE_DEVICES=X python -m footprints.main \
--training_dataset kitti \
--log_path <your_log_path> \
--model_name <your_model_name>and a Matterport model using:
CUDA_VISIBLE_DEVICES=X python -m footprints.main \
--training_dataset matterport \
--height 512 --width 640 \
--log_path <your_log_path> \
--batch_size 8 \
--model_name <your_model_name>If you want to generate your own training data instead of using ours (e.g. you want to try a better ground segmentation algorithm, or more accurate camera poses) then you can!
There are several key elements of our training data - each can be swapped out for your own.
For KITTI we used PSMNet to generate disparity maps for stereo pairs.
These are inside stereo_matching_disps, and are used to generate training labels. These are
converted to depth maps using the known focal length and baseline.
Matterport provides these.
For KITTI we used ORBSLAMv2 to generate camera poses, which are stored as npys inside
the poses folder. These are used to reproject between cameras.
Matterport provides these.
For both Matterport and KITTI we trained a segmentation network to classify ground pixels in an image.
We provide training code for this inside footprints/preprocessing/segmentation. These are stored inside
the ground_seg folder as npys and are unthresholded (i.e. raw sigmoid output).
For KITTI, we identify moving objects by comparing induced flow to optical flow. Our provided optical
flow estimates come from LiteFlowNet, and are inside the optical_flow folder.
Hidden ground depths
To compute hidden depths (i.e. the depth to each visible and occluded ground pixel) we use camera poses, depth maps and ground segmentations. These can be generated using (expects a GPU to be available):
CUDA_VISIBLE_DEVICES=X python -m \
footprints.preprocessing.ground_truth_generation.ground_truth_generator \
--type hidden_depths --data_type kitti --textfile splits/kitti/train.txt
Make sure to run this on both train.txt and val.txt. Warning - this will take a while, so to speed things
up you can do this in parallel by running multiple processes and adding the flags --start_idx X and
--end_idx Y to split the textfile into smaller chunks.
Note that if you have already downloaded our training data, running this command will overwrite it unless you
set --save_folder_name <my_save_folder>. To actually train using this, you can manually set the path inside
footprints/datasets/<kitti or matterport dataset.py>,
or rename your new data to the required folder name, e.g. hidden_depths.
To compute moving objects masks we use optical flow, depth, ground segmentations and camera poses. These can be
generated by amending the above command with --type moving_objects. This is only valid
for KITTI.
Depth masks are estimates of the untraversable pixels in the image, and are computed using
depth maps and ground segmentations. To generate these change the above command to use
--type depth_masks.
To generate predictions for evaluation using a trained model, run:
CUDA_VISIBLE_DEVICES=X python -m footprints.main \
--mode inference \
--load_path <your_model_path, e.g. logs/model/models/weights_9> \
--inference_data_type <kitti or matterport> \
--height <192 for kitti, 512 for matterport> \
--width 640By default this will save to <load_path>/<data_type>_predictions, but can be specified with
--inference_save_path.
To evaluate a folder of predictions, run:
python -m footprints.evaluation.evaluate_model \
--datatype kitti \
--metric iou \
--predictions <path/to/predictions/folder>The following options are provided:
--datatypecan be eitherkittiormatterport.--metriccan beiou(bothkittiandmatterport) ordepth(formatterport)
If necessary, the ground truth files will be automatically downloaded and placed in the ground_truth_files folder.
You can also download the KITTI annotations directly from here.
For each image, there are 3 .png files:
XXXXX_ground.pngcontains the mask of the boundary of visible and hidden ground, ignoring all objectsXXXXX_objects.pngcontains the mask of the ground space taken up by objects (the footprints)XXXXX_combined.pngcontains the full evaluation mask - the visible and hidden ground, taking into account object footprints
We learn from stereo video sequences, using camera poses, per-frame depth and semantic segmentation to form training data, which is used to supervise an image-to-image network.
Results on mobile phone footage:
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More results on the KITTI dataset:
If you find our work useful or interesting, please consider citing our paper:
@inproceedings{watson-2020-footprints,
title = {Footprints and Free Space from a Single Color Image},
author = {Jamie Watson and
Michael Firman and
Aron Monszpart and
Gabriel J. Brostow},
booktitle = {Computer Vision and Pattern Recognition ({CVPR})},
year = {2020}
}
Copyright © Niantic, Inc. 2020. Patent Pending. All rights reserved. Please see the license file for terms.