FlowGRU

This repository contains a TensorFlow implementation for our flowGRU (TITS 2019). Our code is released only for scientific or personal use. Please contact us for commercial use.

Requirements

• TensorFlow 1.4.0
• Cuda 8.0
• Opencv 3.3.1

Getting Started

Datasets

• We conduct experiments on KITTI and Cityscapes. Our method needs additional optical flow. In our framework, DIS-flow is used. For convenience, we provide precomputed flow for the KITTI at here.

Training

• You can train our model using the below command on the specified GPUs by setting CUDA_VISIBLE_DEVICES. Before train the model, you should organize the path for RGB frames as '/path_to_dataset/RGB', flow as '/path_to_dataset/optflw_dis_inv', and ground-truth depth maps as '/path_to_dataset/depth'.

python main.py --data_path '/path_to_dataset'

• We also provide the link for our pre-trained weights trained_on_KITTI and trained_on_Cityscape_and_fine-tuned_on_KITTI.

Test

• Test the trained model with the below command.

python test.py --data_path '/path_to_dataset'
python test_scale.py --data_path '/path_to_dataset'

Citation

Please cite our paper if you find the code useful for your research.

@InProceedings{eom2019temporally,
	        author       = "C. Eom, H. Park, B. Ham",
	        title        = "Temporally Consistent Depth Prediction with Flow-Guided Memory Units",
	        booktitle    = "IEEE transactions on intelligent transportation systems",
	        year         = "2019"
		}