This is the code for the 2018 IJCV paper EMVS: Event-Based Multi-View Stereo - 3D Reconstruction with an Event Camera in Real-Time by Henri Rebecq, Guillermo Gallego, Elias Mueggler, and Davide Scaramuzza.
A pdf of the paper is available here. If you use any of this code, please cite this publication as follows:
@Article{Rebecq18ijcv,
author = {Henri Rebecq and Guillermo Gallego and Elias Mueggler and
Davide Scaramuzza},
title = {{EMVS}: Event-based Multi-View Stereo---{3D} Reconstruction
with an Event Camera in Real-Time},
journal = "Int. J. Comput. Vis.",
year = 2018,
volume = 126,
issue = 12,
pages = {1394--1414},
month = dec,
doi = {10.1007/s11263-017-1050-6}
}-
The proposed EMVS method is patented, as you may find in this link.
H. Rebecq, G. Gallego, D. Scaramuzza Simultaneous Localization and Mapping with an Event Camera Pub. No.: WO/2018/037079. International Application No.: PCT/EP2017/071331 -
The license is available here.
From a high-level, input-output point of view, EMVS receives a set of events and camera poses and produces a semi-dense 3D reconstruction of the scene, as shown in the above video. See the example below.
This software depends on ROS. Installation instructions can be found here. We have tested this software on Ubuntu 16.04 and ROS Kinetic.
Install catkin tools, vcstool:
sudo apt-get install python-catkin-tools python-vcstool
Create a new catkin workspace if needed:
mkdir -p ~/emvs_ws/src && cd ~/emvs_ws/
catkin config --init --mkdirs --extend /opt/ros/kinetic --merge-devel --cmake-args -DCMAKE_BUILD_TYPE=Release
Clone this repository:
cd src/
git clone git@github.com:uzh-rpg/rpg_emvs.git
Clone dependencies:
vcs-import < rpg_emvs/dependencies.yaml
Install pcl-ros:
sudo apt-get install ros-kinetic-pcl-ros
Build the package(s):
catkin build mapper_emvs
source ~/emvs_ws/devel/setup.bash
Download slider_depth.bag data file, from the Event Camera Dataset, which was recorded using the DVS ROS driver.
Run the example:
roscd mapper_emvs
rosrun mapper_emvs run_emvs --bag_filename=/path/to/slider_depth.bag --flagfile=cfg/slider_depth.conf
Configuration parameters: The options that can be passed to the program using the configuration file (e.g., slider_depth.conf) and their default values are defined at the top of the main.cpp file.
These are: the parameters defining the input data, the parameters of the shape and size of the Disparity Space Image (DSI), and the parameters to extract a depth map and its point cloud from the DSI.
Upon running the example above, some images will be saved in the folder where the code was executed, for visualization.
For example, the output images for the slider_depth example should look as follows:
 |
 |
| Confidence map | Depth map |
The depth map is colored according to depth with respect to the reference view.
We also provide Python scripts to inspect the DSI (3D grid).
Install visvis first:
pip install visvis
To visualize the DSI stored in the dsi.npy file, run:
roscd mapper_emvs
python scripts/visualize_dsi_volume.py -i /path/to/dsi.npy
You should get the following output, which you can manipulate interactively:
To visualize the DSI with moving slices (i.e., cross sections), run:
python scripts/visualize_dsi_slices.py -i /path/to/dsi.npy
which should produce the following output:
To visualize the 3D point cloud extracted from the DSI, install pypcd first as follows:
pip install pypcd
and then run:
python scripts/visualize_pointcloud.py -i /path/to/pointcloud.pcd
A 3D matplotlib interactive window like the one below should appear, allowing you to inspect the point cloud (color-coded according to depth with respect to the reference view):
We provide additional examples with sequences from the Event Camera Dataset.
Download dynamic_6dof and run:
rosrun mapper_emvs run_emvs --bag_filename=/path/to/dynamic_6dof.bag --flagfile=cfg/dynamic_6dof.conf
The images generated should coincide with those in this folder.
 |
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| Confidence map | Depth map |
You may also explore the DSI as in the previous example (the same commands should work).
Download boxes_6dof and run:
rosrun mapper_emvs run_emvs --bag_filename=/path/to/boxes_6dof.bag --flagfile=cfg/boxes_6dof.conf
The images generated should coincide with those in this folder.
 |
 |
| Confidence map | Depth map |
You may also explore the DSI as in the previous example (the same commands should work).
Download shapes_6dof and run:
rosrun mapper_emvs run_emvs --bag_filename=/path/to/shapes_6dof.bag --flagfile=cfg/shapes_6dof.conf
The images generated should be those in this folder.
 |
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| Confidence map | Depth map |
As you may notice by inspecting the DSI, the shapes are on a plane (a wall).
By default, the Z slices of the DSI are uniformly spaced in inverse depth.
However, it is possible to change this behavior to use Z slices uniformly spaced in depth (rather than inverse depth).
This can be achieved by changing the option USE_INVERSE_DEPTH to OFF in the CMakeLists.txt.
This requires recompiling mapper_emvs. We recommend removing the emvs_mapper build folder before recompiling.