SfMLearner

This codebase implements the system described in the paper:

Unsupervised Learning of Depth and Ego-Motion from Video

Tinghui Zhou, Matthew Brown, Noah Snavely, David G. Lowe

In CVPR 2017 (Oral).

See the project webpage for more details. Please contact Tinghui Zhou ([email protected]) if you have any questions.

Prerequisites

This codebase was developed and tested with Tensorflow 1.0, CUDA 8.0 and Ubuntu 16.04.

Running the single-view depth demo

We provide the demo code for running our single-view depth prediction model. First, download the pre-trained model by running the following

bash ./models/download_model.sh

Then you can use the provided ipython-notebook demo.ipynb to run the demo.

Preparing training data

In order to train the model using the provided code, the data needs to be formatted in a certain manner.

For KITTI, first download the dataset using this script provided on the official website, and then run the following command

python data/prepare_train_data.py --dataset_dir=/path/to/raw/kitti/dataset/ --dataset_name='kitti_raw_eigen' --dump_root=/path/to/resulting/formatted/data/ --seq_length=3 --img_width=416 --img_height=128 --num_threads=4

For Cityscapes, download the following packages: 1) leftImg8bit_sequence_trainvaltest.zip, 2) camera_trainvaltest.zip. Then run the following command

python data/prepare_train_data.py --dataset_dir=/path/to/cityscapes/dataset/ --dataset_name='cityscapes' --dump_root=/path/to/resulting/formatted/data/ --seq_length=3 --img_width=416 --img_height=171 --num_threads=4

Notice that for Cityscapes the img_height is set to 171 because we crop out the bottom part of the image that contains the car logo, and the resulting image will have height 128.

Training

Once the data are formatted following the above instructions, you should be able to train the model by running the following command

python train.py --dataset_dir=/path/to/the/formatted/data/ --checkpoint_dir=/where/to/store/checkpoints/ --img_width=416 --img_height=128 --batch_size=4 --smooth_weight=0.5 --explain_reg_weight=0.2

You can then start a tensorboard session by

tensorboard --logdir=/path/to/tensorflow/log/files --port=8888

and visualize the training progress by opening https://localhost:8888 on your browser. If everything is set up properly, you should start seeing reasonable depth prediction after ~30K iterations when training on KITTI.

Evaluation on KITTI

Depth

We provide evaluation code for the single-view depth experiment on KITTI. First, download our predictions (~140MB) by

bash ./kitti_eval/download_kitti_depth_predictions.sh

Then run

python kitti_eval/eval_depth.py --kitti_dir=/path/to/raw/kitti/dataset/ --pred_file=kitti_eval/kitti_eigen_depth_predictions.npy

If everything runs properly, you should get the numbers for Ours(CS+K) in Table 1 of the paper. To get the numbers for Ours cap 50m (CS+K), set an additional flag --max_depth=50 when executing the above command.

Pose

We provide evaluation code for the pose estimation experiment on KITTI. First, download the predictions and ground-truth pose data by running

bash ./kitti_eval/download_kitti_pose_eval_data.sh

Notice that all the predictions and ground-truth are 5-frame snippets with the format of timestamp tx ty tz qx qy qz qw consistent with the TUM evaluation toolkit. Then you could run

python kitti_eval/eval_pose.py --gtruth_dir=/directory/of/groundtruth/trajectory/files/ --pred_dir=/directory/of/predicted/trajectory/files/

to obtain the results reported in Table 3 of the paper. For instance, to get the results of Ours for Seq. 10 you could run

python kitti_eval/eval_pose.py --gtruth_dir=kitti_eval/pose_data/ground_truth/10/ --pred_dir=kitti_eval/pose_data/ours_results/10/

Testing code

We also provide sample testing code for obtaining pose predictions on the KITTI dataset with a pre-trained model. You can obtain the predictions formatted as above for pose evaluation by running

python test_kitti_pose.py --test_seq [sequence_id] --dataset_dir /path/to/KITTI/odometry/set/ --output_dir /path/to/output/directory/ --ckpt_file /path/to/pre-trained/model/file/

A sample model trained on 5-frame snippets (this is not the exact model we used for the paper, but performs similarly) can be downloaded by

bash ./models/download_model_5frame.sh

Then you can obtain predictions on, say Seq. 9, by running

python test_kitti_pose.py --test_seq 9 --dataset_dir /path/to/KITTI/odometry/set/ --output_dir /path/to/output/directory/ --ckpt_file models/model-100892

Disclaimer

This is the authors' implementation of the system described in the paper and not an official Google product.