Skip to content
Code for the CVPR paper "CAM-Convs: Camera-Aware Multi-Scale Convolutions for Single-View Depth"
Jupyter Notebook Python
Branch: master
Clone or download
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
ipython Added demo notebook Jun 13, 2019
lmbspecialops @ fb5004d Code of CAMCONVS and DATA AUGMENTATION (Included Video Demo of Data S… Jun 13, 2019
python fixing bug on public implementation of normal loss Sep 8, 2019
.gitignore Code of CAMCONVS and DATA AUGMENTATION (Included Video Demo of Data S… Jun 13, 2019
.gitmodules Code of CAMCONVS and DATA AUGMENTATION (Included Video Demo of Data S… Jun 13, 2019
LICENSE
README.md Update README.md Jun 13, 2019
requirements.txt Add requirements Jun 13, 2019

README.md

CAM-Convs: Camera-Aware Multi-Scale Convolutions for Single-View Depth

Tensorflow implementation of CAM-Convs.

Introduction

This repository contains original implementation of the paper: 'CAM-Convs: Camera-Aware Multi-Scale Convolutions for Single-View Depth' by Jose M. Facil ,Benjamin Ummenhofer, Huizhong Zhou, Luis Montesano, Thomas Brox* and Javier Civera*

The page of the paper is http://webdiis.unizar.es/~jmfacil/camconvs/

Citing

Please cite CFL in your publications if it helps your research:

@InProceedings{Facil_2019_CVPR,
author = {Facil, Jose M. and Ummenhofer, Benjamin and Zhou, Huizhong and Montesano, Luis and Brox, Thomas and Civera, Javier},
title = {{CAM-Convs: Camera-Aware Multi-Scale Convolutions for Single-View Depth}},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}

Use Instructions

We recommend the use of a virtual enviroment for the use of this project. (e.g. pew)

$ pew new venvname -p python3 # replace venvname with your prefered name (it also works with python 2.7)

Install Requirements

1. This code has been compiled and tested using:

  • python3
  • cuda-10.0
  • cuDNN 7.5
  • TensorFlow 1.13

You are free to try different configurations. But we do not ensure it had been tested.

2. Install python requirements:

(venvname)$ pip install -r requirements.txt

3. Compile lmbspecialops

Compile the submodule lmbspecialops following the instrucions here. We recommend to simply run:

(venvname)$ cd lmbspecialops
(venvname)$ python setup.py install
(venvname)$ pew add python
(venvname)$ cd ..

Note: You may need to set the enviroment variable LMBSPECIALOPS_LIB

(venvname)$ export LMBSPECIALOPS_LIB="/path/to/camconvs/lmbspecialops/build/lib.linux-x86_64-3.5/lmbspecialops.so" 

4. Add python folder to the path:

(venvname)$ pew add python/

5. Try our Data Sampling and CAM-Conv Channels demo:

You can run the iPython Notebook and play with our Datawriter, Datareader and data augmentation operations to train CAM-Convs ipython/DEMO_DATA_AUGMENTATION.ipynb.

6. Network code coming soon!

We are planning to add a second exaple including a Network training with multiple cameras.

You can’t perform that action at this time.