Self-Supervised 3D Keypoint Learning for Ego-Motion Estimation

Accepted as Plenary talk at CoRL 2020.

Overview

• Sparse mono-SfM: A new framework for the simultaneous learning of keypoint detection, matching and 3D lifting by incorporating a differentiable pose estimation module.
• Multi-view adaptation: A novel adaptation technique that exploits the temporal context in videos to further boost the repeatability and matching performance of the keypoint network.
• State-of-the-art performance: We integrate the networks into a visual odometry framework, enabling robust and accurate ego-motion estimation results

[Full paper] [YouTube]

Setting up your environment

You need a machine with recent Nvidia drivers and a GPU. We recommend using docker (see nvidia-docker2 instructions) to have a reproducible environment. To setup your environment, type in a terminal (only tested in Ubuntu 18.04):

git clone https://github.com/TRI-ML/KP3D.git
cd KP3D
# if you want to use docker (recommended)
make docker-build

We will list below all commands as if run directly inside our container. To run any of the commands in a container, you can either start the container in interactive mode with make docker-start to land in a shell where you can type those commands, or you can do it in one step:

# single GPU
make docker-run COMMAND="some-command"
# multi-GPU
make docker-run-mpi COMMAND="some-command"

Data

Download the HPatches dataset for evaluation:

cd /data/datasets/kp3d/
wget http://icvl.ee.ic.ac.uk/vbalnt/hpatches/hpatches-sequences-release.tar.gz
tar -xvf hpatches-sequences-release.tar.gz
mv hpatches-sequences-release HPatches

Download the KITTI odometry dataset from here - get the color images and ground truth poses. Unzip the data in /data/datasets/kp3d/KITTI_odometry/.

Pre-trained models:

Download the pre-trained models from here and place them in /data/models/kp3d/.

To replicate our results on the KITTI odometry dataset (Table 1 - Ours), run:

make docker-run-mpi COMMAND="python kp3d/evaluation/evaluate_keypoint_odometry.py --depth_model /data/models/kp3d/depth_resnet.ckpt --keypoint_model /data/models/kp3d/keypoint_resnet.ckpt --dataset_dir /data/datasets/kp3d/KITTI_odometry/dataset/ --output_dir ./pose_output/ --sequence 01 02 06 08 09 10 00 03 04 05 07 --align_trajectory --run_evaluation"

You should get the following results:

Sequence	01	02	06	08	09	10	00	03	04	05	07	Mean Train	Mean Test
t_rel	17.60	3.22	1.84	3.05	2.73	5.08	2.73	3.03	2.21	3.53	2.42	5.58	2.79
r_rel	0.62	1.01	0.75	0.73	0.63	0.97	1.09	2.42	1.97	1.18	1.00	0.79	1.53

To replicate our results on the HPatches dataset (Table 4 - KeypointNet), run:

make docker-run COMMAND="python kp3d/evaluation/evaluate_keypoint_patches.py --pretrained_model /data/models/kp3d/keypoint_resnet.ckpt --input /data/datasets/kp3d/HPatches/"

You should get the following results:

Evaluation for (320, 256):

Repeatability	Localization	C1	C3	C5	MScore
0.686	0.800	0.514	0.867	0.914	0.588

Evaluation for (640, 480):

Repeatability	Localization	C1	C3	C5	MScore
0.674	0.886	0.526	0.857	0.921	0.535

The numbers deviate slightly from the paper, due to different dependency versions.

Trajectories

Trajectories of DS-DSO on KITTI odometry sequences 00-10: ds_dso_kitti_00_10.zip. We also include the results of our ablative analysis as well as our evaluation of monodepth2.

License

The source code is released under the MIT license.

Citation

Please use the following citation when referencing our work:

@inproceedings{tang2020kp3d,
  title = {{Self-Supervised 3D Keypoint Learning for Ego-Motion Estimation}},
  author = {Jiexiong Tang and Rares Ambrus and Vitor Guizilini and Sudeep Pillai and Hanme Kim and Patric Jensfelt and Adrien Gaidon},
  booktitle={Conference on Robot Learning (CoRL)},
  year={2020},
}