Attention
As of June 30, 2025, the Isaac ROS Buildfarm for Isaac ROS 2.1 on Ubuntu 20.04 Focal is no longer supported.
Due to an isolated infrastructure event, all ROS 2 Humble Debian packages that were previously built for Ubuntu 20.04 are no longer available in the Isaac Apt Repository. All artifacts for Isaac ROS 3.0 and later are built and maintained with a more robust pipeline.
Users are encouraged to migrate to the latest version of Isaac ROS. The source code for Isaac ROS 2.1
continues to be available on the release-2.1 branches of the Isaac ROS
GitHub repositories.
The original documentation for Isaac ROS 2.1 is preserved below.
ESS
ESS stands for Efficient Semi-Supervised stereo disparity and was developed by NVIDIA. The ESS DNN is used to predict the disparity for each pixel from stereo camera image pairs. This network has improvements over classic CV approaches that use epipolar geometry to compute disparity, because the DNN can learn to predict disparity in cases where epipolar geometry feature matching fails. The semi-supervised learning and stereo disparity matching makes the ESS DNN robust in environments unseen in the training datasets and with occluded objects. This DNN is optimized for and evaluated with color (RGB) global shutter stereo camera images and accuracy can vary for monochrome stereo images used in analytic computer vision approaches to stereo disparity.
The predicted
disparity values
represent the distance a point moves from one image to the other in a
stereo image pair (a.k.a. the binocular image pair). The disparity is
inversely proportional to the depth, that is, disparity = focalLength x baseline / depth. Given the focal
length and
baseline of the camera
that generates a stereo image pair, the predicted disparity map from the
isaac_ros_ess package can be used to compute depth and generate a
point cloud.
Repositories and Packages
The Isaac ROS implementations of this technology are available here: