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NVIDIA
ESS DNN Stereo Disparity
Model
NVIDIA
NVIDIA
ESS DNN Stereo Disparity

ESS is a DNN that estimates disparity for a stereo image pair and returns a continuous disparity map for the given left image.

This model is backed by NVIDIA's Plus Plus (++) Promise
to learn more about the quality of the datasets used to train this model.
FieldResponse
Intended Application(s) & Domain(s):Building depth, localization, mapping, and navigation capabilities for robotics and autonomous machines.
Model Type:Depth estimation.
Intended Users:Developers building and/or customizing robotics applications
Output:Continuous Disparity Map
Describe how the model works:Predicted continuous disparity map generated from left and right image tensors.
Technical Limitations:Prediction accuracy varies with pixels that are whiter, transparent, and reflective in nature; for sharp boundaries and fine edges; and for flat featureless areas. Visual artifacts can appear in non-overlapping regions and from overfitting.
Verified to have met prescribed NVIDIA standards?Yes.
Performance Metrics:Bad Pixels (Bad Pixels) and Mean Average Error (MAE); Bad Pixel Percentage > 2px.
Potential Known Risks:Imprecise disparity and distance predictions can lead to inability to localize robot and lead to collisions.
Licensing:https://developer.download.nvidia.com/licenses/tao_toolkit_21-08_models_eula.pdf

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