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Fei Xia edited this page Sep 27, 2018 · 8 revisions

Welcome to the GibsonEnv wiki!

FAQ

How can I scan the 3D model of the environment?

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.

  1. you need to store the RGB images of the space as equirectangular panoramas in panos/rgb folder. Note the resolution and aspect ratio.
  2. For each RGB panorama, you need a corresponding depth panorama saved in pano/mist folder.
  3. you need a mesh containing the 3D geometry of the space in obj format saved as mesh.obj and a 90 degree rotated version of it saved as mesh_z_up.obj. The first one is used for image rendering and the second in physics engine.
  4. you need to save the camera pose information for each image in camera_poses.csv and in individual files in panos/points folder.
  5. you need to preserve the naming convention of the existing asset folders.

How can I run Gibson Env in headless mode?

See this guide for more details.

How can I run multiple Gibson instances on one machine?

How can I run Gibson on NGC?

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/gibson
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