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Below is a brief answer, but please note that in Gibson our goal is turning some already scanned and processed places into a virtual perceptual environment. Our focus is not providing a streamlined tool for scanning new buildings, as that's a sufficiently separate problem. We recommend using the hundreds of spaces already included in Gibson database, as its scale should (hopefully) be sufficient for developing agents that generalize to novel spaces.
However, Gibson's format of the asset data of a space (i.e. each folder in gibson/assets/dataset/) is straightforward. As long as you scan a new space and store it following the same format, Gibson will be able to open it. Some of the requirements are listed below. You can scan the space using one of the existing scanning devices, e.g. Paracosm, DotProduct, Matterport, etc. Gibson is agnostic to scanning hardware.
- you need to store the RGB images of the space as equirectangular panoramas in
panos/rgbfolder. Note the resolution and aspect ratio. - For each RGB panorama, you need a corresponding depth panorama saved in
pano/mistfolder. - you need a mesh containing the 3D geometry of the space in obj format saved as
mesh.objand a 90 degree rotated version of it saved asmesh_z_up.obj. The first one is used for image rendering and the second in physics engine. - you need to save the camera pose information for each image in
camera_poses.csvand in individual files inpanos/pointsfolder. - you need to preserve the naming convention of the existing asset folders.
See this guide for more details.
docker pull xf1280/gibson:ngc-0.3.1
docker tag xf1280/gibson:ngc-0.3.1 nvcr.io/<your organization>/gibson_server:0.3.1
docker push nvcr.io/<your organization>/gibson_server:0.3.1
ngc batch run --name "gibson experiment" --image "<your organization>/gibson_server:0.3.1" --ace nv-us-west-2 --instance ngcv8 --commandline "training command" --datasetid <id>:/root/mount/gibson_data --result /tmp/gibsonVisit the guide.