CFA AUSTRALIA WILDFIRE - XVR ENVIRONMENTS

CFA AUSTRALIA WILDFIRE - XVR ENVIRONMENTS

CFA AUSTRALIA WILDFIRE - XVR ENVIRONMENTS

CFA AUSTRALIA WILDFIRE - XVR ENVIRONMENTS

CFA AUSTRALIA WILDFIRE - XVR ENVIRONMENTS

CFA ENVIRONMENTS FOR XVR SIMULATION SOFTWARE

MINDCONSOLE was commissioned to create two Australia-based environments for CFA Wildfire training near Melbourne through XVR Simulation software.  The requirement for both environments was dense, realistic vegetation which proved to be a challenge given that both environments were about ~100 km2 in size.  In the end we placed around 800,000 trees including bushes for the "Plenty Gorge" environment, and around 1,500,000 in the "Strathewen" environment, opening a lot of optimisation requirements to keep the performance intact.

Plenty Gorge  (~96 km2)

Strathewen  (~110 km2)

BothMaps_Infographic_Size

COOPERATION WITH XVR SIMULATION

For the project, a close cooperation with the XVR team in Delft was necessary to guarantee top performance within the XVR software, whilst keeping the quality up to industry standard. Together we found a solution that brought us the best of both worlds. On our recommendation XVR implemented new systems to their core software, making this project possible.

WEATHER AND WILDFIRE FUNCTIONALITY IN XVR

XVR offers a lot of options to show different weather situations. We wanted to support the wind behaviour of the trees as well, which was possible thanks to XVR team implementing it promptly. For the Wildfire functionality included in the XVR Simulation software to work correctly, we had to create the environments in a specific way. The video below shows the possibilities of the current system.

SCREENSHOTS FROM XVR

2020-10-02 12-42-36 GBR_asset_environment_204_aus_plenty_gorge (Large)
2020-10-02 12-44-27 GBR_asset_environment_204_aus_plenty_gorge (Large)
2020-10-02 12-48-18 GBR_asset_environment_204_aus_plenty_gorge (Large)
2020-10-02 12-50-13 GBR_asset_environment_204_aus_plenty_gorge (Large)
2020-10-02 12-52-32 GBR_asset_environment_204_aus_plenty_gorge (Large)
2020-10-02 12-54-27 GBR_asset_environment_204_aus_plenty_gorge (Large)

WILDFIRE SCREENSHOTS

2020-10-02 13-02-39 GBR_asset_environment_204_aus_plenty_gorge (Large)
2020-10-02 12-59-39 GBR_asset_environment_204_aus_plenty_gorge (Large)
2020-10-02 12-58-22 GBR_asset_environment_204_aus_plenty_gorge (Large)
2020-10-02 13-12-45 GBR_asset_environment_204_aus_plenty_gorge (Large)
2020-10-02 13-04-05 GBR_asset_environment_204_aus_plenty_gorge (Large)
2020-10-02 12-57-05 GBR_asset_environment_204_aus_plenty_gorge (Large)

TECHNICAL BREAKDOWN

This project brought a lot of new challenges and questions. MINDCONSOLE had approximately 2 months to finish both environments from scratch and deliver them fully functional. Our approach was to use the procedural nature of modelling which allowed us to create different systems that can change and adjust one small part of the environmental globally. If we build enough systems that depend on each other, we can create complex scenarios that can be quickly iterated & duplicated whilst still being flexible.

Power pole generation

GIF_Powerpoles

The data we received for the power poles was simply a point cloud that included both power poles and street lamps combined. Our task was to separate them from eachother as best as we could, and create connections between the power poles to simulate a real world scenario. The connections do take into account intersections and will adjust themselves depending on the situation and need.

Entry streets generation algorithm

MINDCONSOLE received street data in form of lines which have been used to create regular streets. However, we needed a solution for creating entry ways to all houses, since we havent had this information in delivered data. We have written an algorithm solving this issue, generating entry ways to the houses on our own terms. That way, we can keep the environment somewhat realistic even with "simulated" results.

Building windows and doors

Buildings were the next step for the environment. We needed a generic solution for the creation of simple houses that match the look of typical houses in Australia. Our approach was to create 7 different wall materials, doors on the positions of generated entry roads, windows randomly spread out with some different parameters for each house, and lastly generate generic roofs on each and every building.

GIF_Buildings_Windows_Doors

Terrain adjustments

Our dataset included a heightmap of the area. It did not however account for all of the buildings placed on the terrain as well as created roads  - so we had to adjust the terrain to fit everything necessary, otherwise we would have interpenetrating geometry on all elements of the environment. We built a custom system that fixes all of these issues and passes corrected terrain further down the pipeline.

GIF_TerrainFit

LET'S WORK, TOGETHER

LET'S WORK, TOGETHER

LET'S WORK, TOGETHER

LET'S WORK, TOGETHER

LET'S WORK, TOGETHER

© mindconsole 2020

© mindconsole 2020

© mindconsole 2020

© mindconsole 2020