Variational Fisheye Stereo

Implementation of our ICRA2020(Accepted)/RAL paper for the Realsene T265 fisheye stereo camera.

Menandro Roxas and Takeshi Oishi. Variational Fisheye Stereo. IEEE Robotics and Automation Letters vol. 5-2, pp. 1303-1310, January 17, 2020. IEEExplore Youtube

Update

03/18/2020: Link to convert disparity to depth for the dataset below (equidistant model). Converter-MATLAB. Needs .flo reader.

03/18/2020: Added sample function in main.cpp to use the dataset below (equidistant model). I didn't check if the output folder exist, so create a folder "output" in the main folder of the dataset.

03/18/2020: Added link for MATLAB scipts generating the vector fields KB model, Equidistant model. Needs .flo writer.

02/06/2020: Added 2D disparity to 3D and depth (radial) conversion. copyStereoToHost now requires the intrinsic camera parameters and distortion model coefficients (Kanala-Brandt model).

Requirements

OpenCV, OpenCV Contrib (optflow) (tested with v4.2.0)
CUDA 10.2 (Including Samples for headers)
Visual Studio 2019
Trajectory and Calibration Fields (in .flo format) of the T265 sensor (included). However if you want to use your own T265, use this MATLAB script to generate them: MATLAB. Needs .flo writer.

Building Instructions

The solution consists of two projects - stereotgv and test_vfs. stereotgv generates a static library from which test_vfs links. test_vfs generates a Win32 .exe file.

There is a lib_link.h header (for both project) that links the necessary libraries. Modify the directories:

#define LIB_PATH "D:/dev/lib64/"
#define CV_LIB_PATH "D:/dev/lib64/"
#define CUDA_LIB_PATH "C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v10.1/lib/x64/"

to point to the location of OpenCV (CV_LIB_PATH) and CUDA (CUDA_LIB_PATH) .lib files.

At the same time, modify the Project Properties -> VC++ Directories -> (Executables, Includes, and Libraries) to point to the location of the OpenCV and CUDA builds, too.

Executable: D:/dev/bin
Includes: D:/dev/include
Libraries: D:/dev/lib64

Dataset

Real Dataset (144 image pairs with groundtruth depth): Dataset

The model used in this dataset (equidistant model) is different from the implemented code above and therefore will have a different trajectory and calibration field, which are provided for each image pairs. (TODO: sample code for using the dataset).

Folder Structure:

calib - intrinsic camera matrix K, and transformation R, t between first->second image frames in OpenCV format .xml
image_02/data - first RGB image frame
image_03/data - second RGB image frame
proj_depth/groundtrutn - contains groundtruth depth for image_02
calibrationVector - calibration vector in .flo format
translationVector - trajectory field in .flo format
result_ours - our results for easy comparison (with visualization of disparity error)

Sample Results

INTEL Realsense T265: Video 01 Video 04 Video 07 Video 16 Video 19 Video 20 Video 21

Point cloud from sample image pair: PLY file

To do

*CMake

License

This project is licensed under the MIT license

Author

Menandro Roxas, Machine Learning Researcher, Line Corporation

cleoag / vfs