Awesome

VOID Dataset

Visual Odometry with Inertial and Depth (VOID) dataset

from Unsupervised Depth Completion from Visual Inertial Odometry (in RA-L January 2020 & ICRA 2020)

Authors: Alex Wong, Xiaohan Fei, Stephanie Tsuei

If you use this dataset, please cite our paper:

@article{wong2020unsupervised,
 title={Unsupervised Depth Completion From Visual Inertial Odometry},
  author={Wong, Alex and Fei, Xiaohan and Tsuei, Stephanie and Soatto, Stefano},
  journal={IEEE Robotics and Automation Letters},
  volume={5},
  number={2},
  pages={1899--1906},
  year={2020},
  publisher={IEEE}
}

To follow the VOID sparse-to-dense-depth completion benchmark, please visit: Awesome State of Depth Completion

About the dataset

We propose the VOID dataset for real world use case of depth completion by bootstrapping sparse reconstruction in metric}* space from a VIO system (XIVO).

The dataset was collected using the Intel RealSense D435i camera, which was configured to produce synchronized accelerometer and gyroscope measurements at 400 Hz, along with synchronized VGA-size (640 x 480) RGB and depth streams at 30 Hz. The depth frames are acquired using active stereo and is aligned to the RGB frame using the sensor factory calibration. All the measurements are timestamped.

The dataset contains 56 sequences in total, both indoor and outdoor with challenging motion. Typical scenes include classrooms, offices, stairwells, laboratories, and gardens. Of the 56 sequences, 48 sequences (approximately 47K frames) are designated for training and 8 sequences for testing, from which we sampled 800 frames to construct the testing set. Each sequence constains sparse depth maps at three density levels, 1500, 500 and 150 points, corresponding to 0.5%, 0.15% and 0.05% of VGA size.

Staircase	Classroom
<img src="figures/teaser_stairs.png" width="450">	<img src="figures/teaser_classroom.png" width="450">

Dataset structure

For the release version of the dataset:

void_release
|---- <density>
      |---- data
            |---- <sequence>
                  |---- image
                        |---- <timestamp>.png
                        |---- ...
                  |---- sparse_depth
                        |---- <timestamp>.png
                        |---- ...
                  |---- validity_map
                        |---- <timestamp>.png
                        |---- ...
                  |---- ground_truth
                        |---- <timestamp>.png
                        |---- ...
                  |---- absolute_pose
                        |---- <timestamp>.txt
                        |---- ...
                  |----K.txt
            |---- ...
      |---- train_image.txt
      |---- train_sparse_depth.txt
      |---- train_validity_map.txt
      |---- train_ground_truth.txt
      |---- train_absolute_pose.txt
      |---- train_intrinsics.txt
      |---- test_image.txt
      |---- test_sparse_depth.txt
      |---- test_validity_map.txt
      |---- test_ground_truth.txt
      |---- test_absolute_pose.txt
      |---- test_intrinsics.txt
|---- ...

Densities include 150, 500, and 1500 points, corresponding to the directories void_150, void_500, void_1500, respectively. Text files prefixed with train and test contains the paths for the training and testing sets.

For the raw dataset (rosbags):

void_raw
|---- <sequence>
      |---- dataset
      |---- dataset_500
      |---- dataset_1500
      |---- raw.bag
|---- ...

Files prefixed with dataset are the output of XIVO. The dataset file without the density suffix (``dataset'') denotes the dataset file for 150 points.

Setting up your virtual environment

We will create a virtual environment with the necessary dependencies

virtualenv -p /usr/bin/python3 void-py3env
source void-py3env/bin/activate
pip install numpy opencv-python Pillow matplotlib gdown

Installing ROS (Kinetic)

This is only necessary for processing the raw dataset (rosbag). You may skip this step if you plan on only using the release version.

To install ROS Kinetic:

sudo sh -c 'echo "deb http://packages.ros.org/ros/ubuntu $(lsb_release -sc) main" > /etc/apt/sources.list.d/ros-latest.list'
sudo apt-key adv --keyserver 'hkp://keyserver.ubuntu.com:80' --recv-key C1CF6E31E6BADE8868B172B4F42ED6FBAB17C654
sudo apt-get update
sudo apt-get install ros-kinetic-desktop-full

To install build packages

sudo apt install python-rosinstall python-rosinstall-generator python-wstool build-essential

To enable your ROS environment:

source /opt/ros/kinetic/setup.bash

Downloading VOID

We have migrated from Google Drive to OneDrive

The bash scripts below are now obsolete!

bash/setup_dataset_void.sh
bash/setup_dataset_void_raw.sh

To download the release version of the dataset through your browser you may visit:

https://yaleedu-my.sharepoint.com/:u:/g/personal/alex_wong_yale_edu/Ebwvk0Ji8HhNinmAcKI5vSkBEjJTIWlA8PXwKNQX_FvB7g

Assuming you are in the root of the repository, you can download the dataset via commandline using wget. To construct the same dataset structure as the setup script above:

      
wget -O void_release.zip 'https://yaleedu-my.sharepoint.com/:u:/g/personal/alex_wong_yale_edu/Ebwvk0Ji8HhNinmAcKI5vSkBEjJTIWlA8PXwKNQX_FvB7g?e=0Zqe7g&download=1'
unzip void_release.zip

If you encounter error: invalid zip file with overlapped components (possible zip bomb). Please do the following

export UNZIP_DISABLE_ZIPBOMB_DETECTION=TRUE

and run the above again.

To download the raw VOID dataset (rosbag) through your browser, you may visit:

https://yaleedu-my.sharepoint.com/:u:/g/personal/alex_wong_yale_edu/EcdGqVyQ8DVBqnCX8lUqSDcBRVi0SpV2XwMdYiYbeCzOaA

Assuming you are in the root of the repository, you can download the dataset via commandline using wget. To construct the same dataset structure as the setup script above:

wget -O void_raw.zip 'https://yaleedu-my.sharepoint.com/:u:/g/personal/alex_wong_yale_edu/EcdGqVyQ8DVBqnCX8lUqSDcBRVi0SpV2XwMdYiYbeCzOaA?e=QjCthr&download=1'
unzip void_raw.zip

Loading calibration

Calibration are stored as JSON and text (formatted as JSON) files within the calibration folder.

void_dataset
|---- <calibration>
      |---- calibration.json
      |---- calibration.txt

To read calibration as a map or dictionary:

import os, json

calibration_path = os.path.join('calibration', 'calibration.json')

with open(calibration_path, 'r') as json_file:
    calibration = json.load(json_file)

Note: we use a radtan (plumb bob) distortion model.

The following are the definitions for the calibration parameter names:

f_x, f_y : focal length
c_x, c_y : principal point
k_0, k_1, k_2 : radial distortion coefficients
p_x, p_y : tangential distortion coefficients
b_a, b_g : bias for accelerometer and gyroscope
c_ar, c_as, c_gs, c_gru, c_grl : IMU axis alignment parameters
n_a, n_g : noise for accelerometer and gyroscope
t_camera_to_body : translation vector for camera to imu alignment
w_camera_to_body : rotation (Rodrigues') parameters for camera to IMU alignment

Loading and storing data

To load depth and validity map filepaths:

import data_utils

train_sparse_depth_filepath = 'data/void_1500/train_image.txt'
train_validity_map_filepath = 'data/void_1500/train_image.txt'
train_sparse_depth_paths = data_utils.load_paths(train_sparse_depth_filepath)
train_validity_paths = data_utils.load_paths(train_sparse_depth_filepath)

To load depth and validity maps:

sparse_depth = data_utils.load_depth(train_sparse_depth_paths[0])
validity_map = data_utils.load_validity_map(train_validity_map_paths[0])

To store depth and validity maps:

sparse_depth_outpath = 'sparse_depth.png'
validity_map_outpath = 'validity_map.png'
data_utils.save_depth(sparse_depth)
data_utils.save_validity_map(validity_map)

To read intrinsics or pose (both are store as numpy text files):

import numpy as np
K = np.loadtxt('K.txt')

Related projects

You may also find the following projects useful:

KBNet: Unsupervised Depth Completion with Calibrated Backprojection Layers. A fast (15 ms/frame) and accurate unsupervised sparse-to-dense depth completion method that introduces a calibrated backprojection layer that improves generalization across sensor platforms. This work is published as an oral paper in the International Conference on Computer Vision (ICCV) 2021.
ScaffNet: Learning Topology from Synthetic Data for Unsupervised Depth Completion. An unsupervised sparse-to-dense depth completion method that first learns a map from sparse geometry to an initial dense topology from synthetic data (where ground truth comes for free) and amends the initial estimation by validating against the image. This work is published in the Robotics and Automation Letters (RA-L) 2021 and the International Conference on Robotics and Automation (ICRA) 2021.
AdaFrame: Learning Topology from Synthetic Data for Unsupervised Depth Completion. An adaptive framework for learning unsupervised sparse-to-dense depth completion that balances data fidelity and regularization objectives based on model performance on the data. This work is published in the Robotics and Automation Letters (RA-L) 2021 and the International Conference on Robotics and Automation (ICRA) 2021.
VOICED: Unsupervised Depth Completion from Visual Inertial Odometry. An unsupervised sparse-to-dense depth completion method, developed by the authors. The paper introduces Scaffolding for depth completion and a light-weight network to refine it. This work is published in the Robotics and Automation Letters (RA-L) 2020 and the International Conference on Robotics and Automation (ICRA) 2020.
XIVO: The Visual-Inertial Odometry system developed at UCLA Vision Lab. This work is built on top of XIVO. The VOID dataset used by this work also leverages XIVO to obtain sparse points and camera poses.
GeoSup: Geo-Supervised Visual Depth Prediction. A single image depth prediction method developed by the authors, published in the Robotics and Automation Letters (RA-L) 2019 and the International Conference on Robotics and Automation (ICRA) 2019. This work was awarded Best Paper in Robot Vision at ICRA 2019.
AdaReg: Bilateral Cyclic Constraint and Adaptive Regularization for Unsupervised Monocular Depth Prediction. A single image depth prediction method that introduces adaptive regularization. This work was published in the proceedings of Conference on Computer Vision and Pattern Recognition (CVPR) 2019.

We also have works in adversarial attacks on depth estimation methods and medical image segmentation:

Stereopagnosia: Stereopagnosia: Fooling Stereo Networks with Adversarial Perturbations. Adversarial perturbations for stereo depth estimation, published in the Proceedings of AAAI Conference on Artificial Intelligence (AAAI) 2021.
Targeted Attacks for Monodepth: Targeted Adversarial Perturbations for Monocular Depth Prediction. Targeted adversarial perturbations attacks for monocular depth estimation, published in the proceedings of Neural Information Processing Systems (NeurIPS) 2020.
SPiN : Small Lesion Segmentation in Brain MRIs with Subpixel Embedding. Subpixel architecture for segmenting ischemic stroke brain lesions in MRI images, published in the Proceedings of Medical Image Computing and Computer Assisted Intervention (MICCAI) Brain Lesion Workshop 2021 as an oral paper.

License and disclaimer

This software is property of the UC Regents, and is provided free of charge for research purposes only. It comes with no warranties, expressed or implied, according to these terms and conditions. For commercial use, please contact UCLA TDG.