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 2.45	Download
TFLITE	float	Universal	QAIRT 2.45	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	184.074 ms	6 - 1350 MB	NPU
StereoNet	ONNX	float	Snapdragon® 8 Elite Mobile	218.855 ms	3 - 1320 MB	NPU
StereoNet	ONNX	float	Snapdragon® X2 Elite	181.239 ms	20 - 20 MB	NPU
StereoNet	ONNX	float	Snapdragon® X Elite	331.458 ms	20 - 20 MB	NPU
StereoNet	ONNX	float	Snapdragon® X Elite	331.458 ms	20 - 20 MB	NPU
StereoNet	ONNX	float	Snapdragon® 8 Gen 3 Mobile	260.792 ms	6 - 1985 MB	NPU
StereoNet	ONNX	float	Qualcomm® QCS8550 (Proxy)	354.11 ms	0 - 25 MB	NPU
StereoNet	ONNX	float	Qualcomm® QCS9075	513.647 ms	3 - 6 MB	NPU
StereoNet	ONNX	float	Snapdragon® 8 Elite For Galaxy Mobile	218.855 ms	3 - 1320 MB	NPU
StereoNet	QNN_DLC	float	Snapdragon® 8 Elite Gen 5 Mobile	200.701 ms	3 - 3265 MB	NPU
StereoNet	QNN_DLC	float	Snapdragon® 8 Elite Mobile	236.966 ms	0 - 3240 MB	NPU
StereoNet	QNN_DLC	float	Snapdragon® X2 Elite	192.532 ms	3 - 3 MB	NPU
StereoNet	QNN_DLC	float	Snapdragon® X Elite	361.271 ms	3 - 3 MB	NPU
StereoNet	QNN_DLC	float	Snapdragon® X Elite	361.271 ms	3 - 3 MB	NPU
StereoNet	QNN_DLC	float	Snapdragon® 8 Gen 3 Mobile	285.797 ms	3 - 4453 MB	NPU
StereoNet	QNN_DLC	float	Qualcomm® QCS8275 (Proxy)	1294.205 ms	0 - 3259 MB	NPU
StereoNet	QNN_DLC	float	Qualcomm® QCS8550 (Proxy)	442.28 ms	3 - 6 MB	NPU
StereoNet	QNN_DLC	float	Qualcomm® SA8775P	462.314 ms	0 - 3260 MB	NPU
StereoNet	QNN_DLC	float	Qualcomm® SA8775P	462.314 ms	0 - 3260 MB	NPU
StereoNet	QNN_DLC	float	Qualcomm® SA8775P	462.314 ms	0 - 3260 MB	NPU
StereoNet	QNN_DLC	float	Qualcomm® QCS9075	511.504 ms	3 - 9 MB	NPU
StereoNet	QNN_DLC	float	Qualcomm® SA7255P	1294.205 ms	0 - 3259 MB	NPU
StereoNet	QNN_DLC	float	Qualcomm® SA8295P	515.319 ms	3 - 3369 MB	NPU
StereoNet	QNN_DLC	float	Snapdragon® 8 Elite For Galaxy Mobile	236.966 ms	0 - 3240 MB	NPU
StereoNet	TFLITE	float	Snapdragon® 8 Elite Gen 5 Mobile	297.417 ms	73 - 3824 MB	NPU
StereoNet	TFLITE	float	Snapdragon® 8 Elite Mobile	270.062 ms	73 - 3773 MB	NPU
StereoNet	TFLITE	float	Snapdragon® 8 Gen 3 Mobile	336.95 ms	73 - 5322 MB	NPU
StereoNet	TFLITE	float	Qualcomm® QCS9075	663.537 ms	72 - 202 MB	NPU
StereoNet	TFLITE	float	Snapdragon® 8 Elite For Galaxy Mobile	270.062 ms	73 - 3773 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.