StereoNet: Optimized for Qualcomm Devices

StereoNet is an end-to-end deep architecture for real-time stereo matching that produces high-quality, edge-preserved disparity maps from a rectified stereo image pair.

This is based on the implementation of StereoNet found here. This repository contains pre-exported model files optimized for Qualcomm® devices. You can use the Qualcomm® AI Hub Models library to export with custom configurations. More details on model performance across various devices, can be found here.

Qualcomm AI Hub Models uses Qualcomm AI Hub Workbench to compile, profile, and evaluate this model. Sign up to run these models on a hosted Qualcomm® device.

Getting Started

There are two ways to deploy this model on your device:

Option 1: Download Pre-Exported Models

Below are pre-exported model assets ready for deployment.

Runtime	Precision	Chipset	SDK Versions	Download
ONNX	float	Universal	QAIRT 2.42, ONNX Runtime 1.24.3	Download
QNN_DLC	float	Universal	QAIRT 2.43	Download
TFLITE	float	Universal	QAIRT 2.43, TFLite 2.19.1	Download

For more device-specific assets and performance metrics, visit StereoNet on Qualcomm® AI Hub.

Option 2: Export with Custom Configurations

Use the Qualcomm® AI Hub Models Python library to compile and export the model with your own:

• Custom weights (e.g., fine-tuned checkpoints)
• Custom input shapes
• Target device and runtime configurations

This option is ideal if you need to customize the model beyond the default configuration provided here.

See our repository for StereoNet on GitHub for usage instructions.

Model Details

Model Type: Model_use_case.depth_estimation

Model Stats:

• Model checkpoint: KeystoneDepth (epoch=21-step=696366.ckpt)
• Input resolution: 786x490
• Number of parameters: 1.94M
• Model size (float): 7.41 MB

Performance Summary

Model	Runtime	Precision	Chipset	Inference Time (ms)	Peak Memory Range (MB)	Primary Compute Unit
StereoNet	ONNX	float	Snapdragon® 8 Elite Gen 5 Mobile	184.723 ms	6 - 1359 MB	NPU
StereoNet	ONNX	float	Snapdragon® X2 Elite	366.346 ms	20 - 20 MB	NPU
StereoNet	ONNX	float	Snapdragon® X Elite	331.059 ms	19 - 19 MB	NPU
StereoNet	ONNX	float	Snapdragon® 8 Gen 3 Mobile	261.303 ms	6 - 1981 MB	NPU
StereoNet	ONNX	float	Qualcomm® QCS8550 (Proxy)	392.531 ms	0 - 25 MB	NPU
StereoNet	ONNX	float	Qualcomm® QCS9075	513.376 ms	3 - 6 MB	NPU
StereoNet	ONNX	float	Snapdragon® 8 Elite For Galaxy Mobile	218.871 ms	3 - 1320 MB	NPU
StereoNet	QNN_DLC	float	Snapdragon® 8 Elite Gen 5 Mobile	173.6 ms	3 - 1358 MB	NPU
StereoNet	QNN_DLC	float	Snapdragon® X2 Elite	162.354 ms	3 - 3 MB	NPU
StereoNet	QNN_DLC	float	Snapdragon® X Elite	313.62 ms	3 - 3 MB	NPU
StereoNet	QNN_DLC	float	Snapdragon® 8 Gen 3 Mobile	247.929 ms	3 - 1976 MB	NPU
StereoNet	QNN_DLC	float	Qualcomm® QCS8275 (Proxy)	1207.787 ms	1 - 1349 MB	NPU
StereoNet	QNN_DLC	float	Qualcomm® QCS8550 (Proxy)	374.062 ms	3 - 6 MB	NPU
StereoNet	QNN_DLC	float	Qualcomm® SA8775P	404.265 ms	1 - 1348 MB	NPU
StereoNet	QNN_DLC	float	Qualcomm® QCS9075	491.28 ms	3 - 9 MB	NPU
StereoNet	QNN_DLC	float	Qualcomm® QCS8450 (Proxy)	725.806 ms	3 - 2152 MB	NPU
StereoNet	QNN_DLC	float	Qualcomm® SA7255P	1207.787 ms	1 - 1349 MB	NPU
StereoNet	QNN_DLC	float	Qualcomm® SA8295P	474.346 ms	0 - 1499 MB	NPU
StereoNet	QNN_DLC	float	Snapdragon® 8 Elite For Galaxy Mobile	203.368 ms	3 - 1334 MB	NPU
StereoNet	TFLITE	float	Snapdragon® 8 Elite Gen 5 Mobile	238.584 ms	72 - 1691 MB	NPU
StereoNet	TFLITE	float	Snapdragon® 8 Gen 3 Mobile	309.589 ms	72 - 2206 MB	NPU
StereoNet	TFLITE	float	Qualcomm® QCS8275 (Proxy)	1322.818 ms	74 - 1629 MB	NPU
StereoNet	TFLITE	float	Qualcomm® QCS8550 (Proxy)	442.285 ms	74 - 78 MB	NPU
StereoNet	TFLITE	float	Qualcomm® SA8775P	528.953 ms	74 - 1629 MB	NPU
StereoNet	TFLITE	float	Qualcomm® QCS9075	639.633 ms	72 - 177 MB	NPU
StereoNet	TFLITE	float	Qualcomm® QCS8450 (Proxy)	758.738 ms	75 - 2482 MB	NPU
StereoNet	TFLITE	float	Qualcomm® SA7255P	1322.818 ms	74 - 1629 MB	NPU
StereoNet	TFLITE	float	Qualcomm® SA8295P	562.268 ms	74 - 1726 MB	NPU
StereoNet	TFLITE	float	Snapdragon® 8 Elite For Galaxy Mobile	256.504 ms	52 - 1639 MB	NPU

License

• The license for the original implementation of StereoNet can be found here.

References

• StereoNet: Guided Hierarchical Refinement for Real-Time Edge-Aware Depth Prediction
• Source Model Implementation

Community

• Join our AI Hub Slack community to collaborate, post questions and learn more about on-device AI.
• For questions or feedback please reach out to us.