/practical-multi-view

Practical Multi-Camera Computer Vision and Algorithms

Primary LanguageC++

Multi-Camera Computer Vision and Algorithms

This is an implementation for the practical course Multi-Camera Computer Vision and Algorithms at TUM.

Demonstration

Watch the pipeline run on the KITTI benchmark on Youtube

Requirements

  • OpenCV 3.3 or later

  • Ceres 1.13 or later

  • Dlib 19.9 or later

Build

mkdir build
cdir build
cmake ..
cmake --build .

Configuration

Run binary with path to configuration file as argument. The configuration may look like this:

[Settings]
fancy_video = 1
verbose     = 1
video_path  = ../../../tracker.avi
error_path  = ../../../error.txt
[Odometry]
; When extracting 2d features, tries to extract at least this amount
min_tracked_features = 400
; Tolerated number of seen 3d points before triangulating new features
tracked_features_tol = 150
; Number of frames used to initialise odometry pipeline
init_frames          = 5
; Number of frames to track
frames               = 600
; Number of frames used for bundle adjustment
bundle_size          = 5
[ceres]
max_iterations = 5
[KITTI]
image_dir          = D:\Odometry\dataset\sequences\07\image_0
; Number of camera in calibration file
camera             = 0
camera_calibration = D:\Odometry\dataset\sequences\07\calib.txt
poses              = D:\Odometry\dataset\poses\07.txt