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This project enables you to utilize event cameras to carry out live depth estimations from images projected with a laser projector. We've streamlined the depth estimation process by creating a lookup image with one spatial and one temporal axis (y and t), forming an X-map. This idea enables speedy depth calculations (taking less than 3 ms per frame), but also maintains the accuracy of depth estimation through disparity search in time maps. The end result is an efficient, reactive tool for designing real-time Spatial Augmented Reality experiences.

The entry point for the live depth estimation is python/depth_reprojection.py. The script is using the Metavision SDK to facilitate event data capture from Prophesee cameras. For a straightforward environment setup, an Ubuntu 20.04 Dockerfile is provided. The depth estimation is implemented in Python with NumPy and Numba.

Getting Started

The project is configured to run from a Docker image in Visual Studio Code (VS Code). It was tested on an Ubuntu 22.04 host.

Installation

1. Clone the repo

   git clone git@github.com:fraunhoferhhi/X-maps.git
   

2. Open X-maps folder in VS Code
3. Install the extensions recommended by VS Code (Docker and Dev Containers)
4. Copy .devcontainer/metavision.list.template to .devcontainer/metavision.list
5. Edit .devcontainer/metavision.list to fill in the URL to the Ubuntu 20.04 Metavision SDK
6. Reopen in Container in VS Code

Download ESL Raw Data for Testing

To use data from the ESL: Event-based Structured Light dataset locally:

1. Create a local folder on the host machine to store the data, e.g. /data/2022_ESL_Event_based_Structured_Light.
2. Add a "mounts" entry in devcontainer.json, that mounts the local folder to /ESL_data in the container.
3. Rebuild container to reopen the project with the mounted folder.
4. Terminal → Run Task... → Download ESL (static) raw and bias files.

ESL static depth reprojection

ESL book duck sequence depth estimation, from the camera perspective

Run the target X-maps ESL static seq1. A window should open that performs a live depth estimation of the book_duck sequence, rendered by default from the projector's perspective.

Live depth reprojection (Spatial Augmented Reality example)

1. Ensure that the camera is working correctly by running metavision_player in a Terminal in VS Code.
2. Calibrate your camera-projector setup, and write the parameters into a YAML file, storing the OpenCV matrices. Examples can be found in data/.
3. Choose camera biases in a way to produce mostly positive events (negative are ignored in processing) and produce few events outside the projected area.
4. Edit the command line arguments for target X-maps live depth reprojection in .vscode/launch.json.
5. Display bright content on the projector to allow the start and end of the frame to be identified (trigger finding).
6. Running X-maps live depth reprojection creates a window that shows the scene depth from the projector's view.
7. Move the depth reprojection window to the projector to overlay the scene with the measured depth.

To display the depth in full screen on the projector, use the OS window manager to maximize the window. On Ubuntu, a keyboard shortcut can be set under Settings → Keyboard → View and Customize Shortcuts → Windows → Toggle fullscreen mode.

The parameters you can use when running the depth_reprojection.py script can be listed by running python3 python/depth_reprojection.py --help in a Terminal in the Docker image in VS Code.

Compute evaluation on static ESL data

As there is no dataset providing ground truth depth, we chose to check the correctness of X-Maps depth estimation by comparing it to the results of ESL, which performs a disparity search in time maps followed by an lengthy point-wise optimization. The following steps describe how to download the ESL time maps, process them with the MC3D implementation of ESL, ESL init, and finally X-Maps. After all steps are completed, Table 1 of the paper will be printed.

1. Create a local folder on the host machine to store the data, e.g. /data/2022_ESL_Event_based_Structured_Light.
2. Add a "mounts" entry in devcontainer.json, that mounts the local folder to /ESL_data in the container.
3. Rebuild container to reopen the project with the mounted folder.
4. Terminal → Run Task... → Run X-maps evaluation script.

Technical details

Calibration

Calibration of the camera-projector system needs to be provided by the user. The software uses OpenCV coordinate system and OpenCV camera calibration parameters read from a YAML file. Unit is cm.

Additional information on our calibration method is provided in the master thesis linked in the section Further reading below.

Differences to ESL

The X-Maps column lists the default for the depth reprojection script. The ESL column lists the default for the ESL implementation. For comparison against esl_init in the evaluation, the ESL assumptions are used when processing with X-Maps.

Depth reprojection steps

Here we describe the steps that are performed by the depth reprojection script. It starts with the raw events coming from the camera. By default, the SDK is queried to get 4 packets of events per projector frame.

Frame dropping

Unless --no-frame-dropping is specified, TimingWatchdog will measure how far behind the CPU time is behind the event time of the current events. If the CPU is more than one frame behind, it schedule that one frame worth of event packets are dropped.

Polarity filter

Only positive events are kept, remaining negative events are dropped.

Activity noise filter

Isolated events are removed, as they are likely to be noise outside the projected area.

Trigger finder

The trigger finder collects incoming events until it has at least one projector frame worth of events. It then searches for an uninterrupted sequence of events at least half a frame length and passes these events on.

Duplicate coordinate filter

The processing of the disparity is completely event-based. In time-map-based processing, events within one frame that share the same set of coordinates are implicitly removed. The FrameEventFilter can be used to remove duplicate events. By default it does not filter anything.

X-Maps disparity lookup

The rectified y coordinate of the event together with its scaled time are used to lookup the corresponding x value in the projector X-Map. The diffence from rectified the camera x coordinate is the disparity.

Disparity map

The disparity values are stored in a rectified map, dilated with a (7,7) filter and remapped to the projector view.

When using --camera-perspective, the disparity values are directly stored in the camera view at the original event coordinates.

Depth coloring

The disparity map is converted to a depth map using the camera-projector calibration. The depth map is clipped to the z_near and z_far parameters and converted to a color image.

Performance considerations

Here are a few things to try if the processing speed lags behind the projector:

• The default projector perspective contains more processing steps than the camera perspective, which can just use the event pixel coordinates. Use --camera-perspective to speed up processing.
• The event package length delta_t is set to produce four packages per processor frame. Reduce EV_PACKETS_PER_FRAME to one (or even lower) to run the main loop fewer times per frame.
• The individual processing steps are running parallelized numba kernels. On some machines (e.g. laptops), you may seem improved performance by reducing the number of threads. You can achieve that by adding the following lines to the configuration in the launch.json, in this example capping the parallel processing to 4 threads:

  "env": {
      "NUMBA_NUM_THREADS": "4",
  }

Further reading

For comprehensive background and detailed methodologies showing our camera/projector setup, discussing our calibation method in detail, and providing additional experiments, refer to the master thesis of our co-author Simon Baumann: 3D reconstruction with structured light using event-based vision and a video laser projector. In this work, the same experimental setup is used as in the paper. It differs in the disparity calculation, where it provides a CUDA implementation of the ESL-init algorithm. The CUDA implementation from the thesis is compared against in the X-Maps paper, in Table 2, as "ESL-init (CUDA)".

Citation

If you find this work useful, a citation will be appreciated:

@inproceedings{morgenstern2023cvpr,
  title        = {X-maps: Direct Depth Lookup for Event-based Structured Light Systems},
  author       = {Morgenstern, Wieland and Gard, Niklas and Baumann, Simon and Hilsmann, Anna and Eisert, Peter},
  year         = 2023,
  booktitle    = {2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)},
  volume       = {},
  number       = {},
  pages        = {4007--4015},
  doi          = {10.1109/CVPRW59228.2023.00418},
  keywords     = {Computer vision;Codes;Conferences;Lasers;Spatial augmented reality;Estimation;Vision sensors}
}

License

Distributed under the GPL-3.0 license. See LICENSE for more information.

Contact

Wieland Morgenstern: LinkedIn · Fraunhofer HHI homepage

Acknowledgments

• ESL: Event-based Structured Light
• Metavision SDK
• Event-based Vision Resources
• CVPR 2023 Workshop on Event-based Vision