DVS Image Reconstruction
This repository contains (1) Complementary filter (combines events and frames) and (2) High pass filter (pure event reconstruction). It can be used to reconstruct a continuous-time image representation of the event stream.
This package was developed using ROS version Kinetic (Ubuntu 16.04).
It can be run in real-time using (1) live DVS (RPG Event Camera Driver), or (2) pre-recorded rosbag (data, Color Event Dataset).
Got a noisy rosbag? Try the hot pixel filter.
The jupyter notebook demo version is now available.
The source code is released under the MIT License.
- Cedric Scheerlinck, Nick Barnes, Robert Mahony, "Continuous-time Intensity Estimation Using Event Cameras", Asian Conference on Computer Vision (ACCV), Perth, 2018.
Please replace <YOUR VERSION> with your ROS version (e.g. kinetic).
sudo apt install libusb-1.0-0-dev python-catkin-tools python-vcstool dh-autoreconf
Install ROS dependencies:
sudo apt install ros-<YOUR VERSION>-camera-info-manager ros-<YOUR VERSION>-image-view
Create a new catkin workspace if needed:
mkdir -p ~/catkin_ws/src && cd ~/catkin_ws/ catkin config --init --mkdirs --extend /opt/ros/<YOUR VERSION> --merge-devel --cmake-args -DCMAKE_BUILD_TYPE=Release
Clone this repository:
cd src/ git clone https://github.com/cedric-scheerlinck/dvs_image_reconstruction.git
vcs-import < dvs_image_reconstruction/dependencies.yaml
cd rpg_dvs_ros/libcaer_catkin/ sudo ./install.sh
Build the packages:
catkin build davis_ros_driver complementary_filter pure_event_reconstruction source ~/catkin_ws/devel/setup.bash
The webpage for this project with links to data, slides, paper and more can be found here:
This research was funded by an Australian Government Research Training Program Scholarship (AGRTP) and the Autralian Research Council through the Australian Centre of Excellence for Robotic Vision (ACRV) CE140100016.