Implementation of the methods in "Learning Depth from Monocular Videos using Direct Methods". If you find this code useful, please cite our paper:

@InProceedings{Wang_2018_CVPR,
author = {Wang, Chaoyang and Miguel Buenaposada, José and Zhu, Rui and Lucey, Simon},
title = {Learning Depth From Monocular Videos Using Direct Methods},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2018}
}

• Python 3.6

• PyTorch 0.3.1 (latter or eariler version of Pytorch is non-compatible.)

• visdom, dominate

We refer "SfMLeaner" to prepare the training data from KITTI. We assume the processed data is put in directory "./data_kitti/".

Start visdom server before for inspecting learning progress before starting the training process.

python -m visdom.server -port 8009

1. train from scratch with PoseNet

bash run_train_posenet.sh

see run_train_posenet.sh for details.

2. finetune with DDVO

Use pretrained posenet to give initialization for DDVO. Corresponds to the results reported as "PoseNet+DDVO" in the paper.

bash run_train_finetune.sh

see run_train_finetune.sh for details.

• Pretrained depth network reported as "Posenet-DDVO(CS+K)" in the paper [download].

• Depth prediction results on KITTI eigen test split(see Table 1 in the paper): [Posenet(K)], [DDVO(K)], [Posenet+DDVO(K)],[Posenet+DDVO(CS+K)]

• To test yourself:

CUDA_VISIBLE_DEVICES=0 nice -10 python src/testKITTI.py --dataset_root $DATAROOT --ckpt_file $CKPT --output_path $OUTPUT --test_file_list test_files_eigen.txt

We again refer to "SfMLeaner" for their evaluation code.

Part of the code structure is borrowed from "Pytorch CycleGAN"