通过几何感知对抗学习进行动态对象移除和时空RGB-D修复.zip

# DynaFill [Project](http://inpainting.cs.uni-freiburg.de/) | [arXiv](https://arxiv.org/abs/2008.05058) Borna Bešić, Abhinav Valada _Dynamic Object Removal and Spatio-Temporal RGB-D Inpainting via Geometry-Aware Adversarial Learning_  ## Setting Up the Environment We recommend using [conda](https://docs.conda.io/en/latest/) package and environment management system. We provide [`environment.yml`](environment.yml) that can be used to easily create a self-contained environment with all the dependencies: ```sh conda env create -f environment.yml conda activate DynaFill ``` #### Tested Configuration - Linux 4.15.0-122-generic x86_64 - NVIDIA GPU Driver 390.138 + CUDA 9.0 - Python 3.6 - PyTorch 1.1.0 + torchvision 0.2.1 - OpenCV 4.0 ## Dataset The description of our DynaFill dataset with the corresponding download instructions can be found at [inpainting.cs.uni-freiburg.de/#dataset](http://inpainting.cs.uni-freiburg.de/#dataset). ## Running the Demo ```sh usage: demo.py [-h] [--device DEVICE] dataset_split_dir positional arguments: dataset_split_dir Path to training/ or validation/ directory of DynaFill dataset optional arguments: -h, --help show this help message and exit --device DEVICE Device on which to run inference ``` #### Example ```sh python demo.py /mnt/data/DynaFill/validation --device cuda:1 ``` ## Citation If you find the code useful for your research, please consider citing our paper: ``` @article{bei2020dynamic, title={Dynamic Object Removal and Spatio-Temporal RGB-D Inpainting via Geometry-Aware Adversarial Learning}, author={Borna Bešić and Abhinav Valada}, journal={arXiv preprint arXiv:2008.05058}, year={2020} } ``` ## License For academic usage, the code is released under the [GPLv3 license](LICENSE). For any commercial purpose, please contact the authors.