This repository contains the code for the following paper:
OASIS: A Large-Scale Dataset for Single Image 3D in the Wild,
Weifeng Chen, Shengyi Qian, David Fan, Noriyuki Kojima, Max Hamilton, Jia Deng
Conference on Computer Vision and Pattern Recognition (CVPR), 2020.
Please check the project site for more details.
The code has been tested on python 3.7, cuda 10.0, pytorch 1.1.0, gcc 8.4.0
conda create --name oasis python=3.7
conda activate oasis
conda install pytorch==1.1.0 torchvision==0.3.0 cudatoolkit=10.0 -c pytorch
conda install opencv==3.4.2 h5py scipy pillow==6.1.0 scikit-learn
pip install sacred easydict pyyaml imageio==2.6.0 tb-nightly future tqdm
Please go to the download page and download all the images and annotations. Then untar:
mkdir OASIS
tar -xzf OASIS_images_v1.tar.gz -C OASIS
tar -xzf OASIS_trainval_annotations_v1.tar.gz -C OASIS
The folder tree after these steps should look like:
OASIS
- LICENSE
- OASIS_trainval
- image
- meta
- OASIS_train.csv
- OASIS_val.csv
- depth
- normal
- fold
- occlusion
- mask
- DIW_style_rel_depth
- segmentation
- planar_instance
- continuous_instance
- OASIS_test
- image
- meta
- OASIS_test.csv
To submit your predictions to the OASIS benchmark, store your predictions for all test images in a directory with the following format:
<parent-dir>/
- *****.npy
- *****.npy
...
- *****.npy
Where <parent-dir>
is one of depth
, occfold
, normals
or planar
, depending on which benchmark you are submitting to. Then run the upload_to_benchmark.py python script to submit your results to the leaderboard. This can take upwards of an hour on slower internet connections.
Example:
python upload_to_benchmark.py --task normal_bench \
--password 9e67a7866dtf484748fcaf07fh5724s4etc7b94c --public \
--email [email protected] --submission_name Hourglass --affiliation Princeton
Usage:
positional arguments:
submission_directory The directory containing .npy files to tar and submit.
optional arguments:
-h, --help show this help message and exit
--task TASK one of ['normal_bench', 'depth_bench',
'occfold_bench', 'planar_bench'].
--affiliation AFFILIATION
Your Affiliation (will not be publicly displayed).
--publication_title PUBLICATION_TITLE
Publication Title.
--publication_url PUBLICATION_URL
Link to Publication.
--authors AUTHORS Authors.
--submission_name SUBMISSION_NAME
Submission Name (The name that will appear on the
leaderboard).
--email EMAIL Email account entered when receiving a password for
OASIS.
--password PASSWORD OASIS account password. Requested via the OASIS login
page. Valid for four hours.
--public Make the submission public.
--skip_taring Assume the submission is already tarred into the temporary
directory.
The experiment
folder contains code to reproduce the results for the following experiments:
- Depth Estimation
- Surface Normal Estimation
- Fold and Occlusion Boundary Detection
- Planar Instance Segmentation
Please refer to the README files under each folder for instructions on how to run the code.
To run on pretrained models, please first download the pretrained models experiments.tar.gz, and tar -xzf experiments.tar.gz
.