LIDAR accelerates virtual building

Anyone who has worked on the production side of virtual environments can attest that content creation and scene development is a costly and time consuming endeavor.

Depending on the complexity and fidelity of the scene, production can easily become the highest expense of the project. In more traditional virtual environments such as Unreal 3, CryEngine, Big World, Arma3, and Delta3D, highly skilled graphic artists are required to create the static meshes using Maya, 3DSMax, or Blender.

People have to create textures that are in alignment with resolution and quality standards of the target platform. The process is much like a movie production and takes exhaustive planning.

The Second Life and OpenSimulator platforms are different. Content creation has been democratized to allow anyone with the will to learn to contribute to a scene. The labor costs may be lower but the time to produce can still be extended. Further, high quality content still has to come from experienced creators.

A labor-saving approach

STTC_VirtualEnvironmentLogo-FinalThe US Army Research Laboratory’s Simulation and Training Technology Center has been experimenting with automation to try to reduce the time it takes to create and ingest content into a virtual environment. The objective of this line of research is to attempt to reduce the amount of time and labor — in other words, money — it takes to produce a usable virtual recreation of a real world environment, called an operational environment.

“Usable” is defined as equally interactive as a traditionally produced static mesh object or primitive-based build. For example, a house imported into the scene using the automated process would still need to have working doors and interior spaces.

The Technology Center recently funded the University of Central Florida’s Institute for Simulation and Training to investigate the use of ground based LIDAR — Laser Imaging, Detection and Ranging — to rapidly scan an operational environment and produce a scene for a virtual environment.

The workflow that has resulted from this investigation has produced valuable information we have used to recreate areas of the University of Central Florida campus.

The first step in the workflow is to scan the operational environment. The university chose to use the FARO scanning system mounted on a tripod to scan an area  located in the Orlando Research Park. Multiple scans were performed and later stitched together in software. The resulting point cloud can be seen in the image below. The FARO system also includes the ability to capture digital photos so that textures can be created later.

Point cloud model of the laser-scanned scene. (Image courtesy Douglas Maxwell.)

Point cloud model of the laser-scanned scene. (Image courtesy Douglas Maxwell.)

The next step in the workflow process is to convert the point cloud data into a mesh. This is a heavy task for a computer and takes many hours of processing on a modern desktop PC. The software used for this task was and the output is shown below.

A high-polygon mesh model created from the previous point cloud. (Image courtesy Douglas Maxwell.)

A high-polygon mesh model created from the previous point cloud. (Image courtesy Douglas Maxwell.)

Even though this is now a mesh model, the polygon count is in the millions and much too high to be usable in a virtual environment.

The third step in the workflow is to decompose this high density model into a lower polygon count mesh model using Maya.

After the decomposition is complete, the model is saved to either a FBX format or Collada 1.4 file. A rendering of the lower polygon count model is shown below.

A mesh model with fewer polygons after processing in Maya. (Image courtesy Douglas Maxwell.)

A mesh model with fewer polygons after processing in Maya. (Image courtesy Douglas Maxwell.)

Since the output of this last step of the workflow is a generic mesh model, it can be imported into any modern game or virtual environment engine.

To test this proof of concept, we used the Military Open Simulator Enterprise Strategy grid, also known as MOSES. We imported the Collada mesh file to a standard region and added background props such as foliage.

Basic textures were applied and a simple scene was created, which you can see below. The two multi-story buildings reside within a fenced area and the entire footprint fits on a single standard sim at 1:1 scale.

Screenshot of the build on the MOSES grid. (Image courtesy Douglas Maxwell.)

Screenshot of the build on the MOSES grid. (Image courtesy Douglas Maxwell.)

This proof of concept is considered a success and we plan to continue the exploration of using ground based LIDAR technology for more testing and evaluation.

The uses for this technique go beyond military applications.

This technology could be applied to rapidly create any large space for use in domain–relevant training for first responders, for example. Instead of generic office buildings and urban landscapes, first responders could be presented with a virtual environment that closely represents their actual areas of responsibility.

Using this technique, early workflow data indicate a reduction of effort by over 60 percent as compared to the creation of a similar scenario using traditional techniques. It is believed that with further investigation, the amount of effort it takes to produce a similar scene could be reduced.

Dr. Charlie Hughes, Dr. Lori Walters, and Alexi Zelenin at the University of Central Florida’s Synthetic Reality Lab contributed to this project, as did Gwenette Sinclair and Jessica Compton of the Virtual Worlds Research Lab at the US Army Research Laboratory’s Simulation and Training Technology Center.

OpenSim users may download a copy of an OAR export file of the region discussed in this article from our website here:

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Douglas Maxwell

Douglas Maxwell is the science and technology manager for virtual world strategic applications at the U.S. Army's Simulation & Training Technology Center.

5 Responses

  1.' lmpierce says:

    Interesting article, and thank you for the oar file example…

    Part of the cost question relates to time, but the costs of access are also important. In the case of LIDAR, its applications are crucial when accuracy is critical, and when there is a sufficient economy of scale. But I would suggest that for smaller units of production, there is probably not enough money or expertise, as LIDAR is very expensive and not easy to make use of. The tipping point in value is higher than the article suggests.

    For circumstances where good, but not perfect realism is required, there are photographic techniques, such as photogrammetry. During a recent course I attended in digital sets, we photographed a building with Canon DSLRs and recreated the building by uploading those photographs to Autodesk, where they were put together and mapped onto a 3D mesh. With a bit of clean-up, we had a fairly accurate representation of a structure, useful for all kinds of simulations. There is a free consumer service here for exploration:

    But not to propose an either or dichotomy, I’m suggesting that for accessibility by those working on small budgets, photogrammetry is labor-saving over meticulous hand building, just as LIDAR is labor saving over meticulous hand building, yet photogrammetry is readily available for much lower costs. This seems especially important to promote for users of OpenSim.

    •' Douglas Maxwell says:

      You make a great point. We use LIDAR for large areas, such as buildings and city blocks. Later this year we plan to scan a MOUT training site. We are also investigating photogrammetry for the rapid ingestion of small objects and people. We have a prototype we use for heads that used 10 point and shoot cameras sync’d by USB (image attached). We plan to create a full body rig this year using 50 cameras. When its ready, I”ll release more info on the progress.

      •' Darren Conley says:

        Are you able to get a wide enough area of coverage for the entire head using the setup in the photo above? Or are you only concerned about the face and can build the back and top of the head manually after capture?

  2.' Paul E. Emery says:

    I just tried the 123d catch program mentioned by Impierce. and was able to make the sample budda mesh modle. Then used blender to change it from obj to dae and import into opensim using firestorm. Actually very cool of course now i have to learn how to make the mesh texture. :-((

  3.' Christopher says:

    Can you share any of your research? I am working on a robot project using the Intel RealSense to create a pointcloud and am trying to figure out how to map it into an OpenSim world environment that is continually built as the robot explores the real world environment both Internal building and external environments.