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library_name: pytorch
license: other
tags:
- real_time
- bu_auto
- android
pipeline_tag: other
---
# RangeNet-Plus-Plus: Optimized for Qualcomm Devices
RangeNet-Plus-Plus (also stylized as RangeNet++) projects a LiDAR point cloud onto a 5-channel range image (depth, x, y, z, intensity) and applies a DarkNet-53 encoder with a decoder head to predict per-point semantic class labels in real time.
This is based on the implementation of RangeNet-Plus-Plus found [here](https://github.com/PRBonn/lidar-bonnetal).
This repository contains pre-exported model files optimized for Qualcomm® devices. You can use the [Qualcomm® AI Hub Models](https://github.com/qualcomm/ai-hub-models/blob/v0.57.1/src/qai_hub_models/models/rangenet_plus_plus) library to export with custom configurations. More details on model performance across various devices, can be found [here](#performance-summary).
Qualcomm AI Hub Models uses [Qualcomm AI Hub Workbench](https://workbench.aihub.qualcomm.com) to compile, profile, and evaluate this model. [Sign up](https://myaccount.qualcomm.com/signup) 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 2.45, ONNX Runtime 1.25.0 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/rangenet_plus_plus/releases/v0.57.1/rangenet_plus_plus-onnx-float.zip)
| TFLITE | float | Universal | QAIRT 2.45 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/rangenet_plus_plus/releases/v0.57.1/rangenet_plus_plus-tflite-float.zip)
For more device-specific assets and performance metrics, visit **[RangeNet-Plus-Plus on Qualcomm® AI Hub](https://aihub.qualcomm.com/models/rangenet_plus_plus)**.
### Option 2: Export with Custom Configurations
Use the [Qualcomm® AI Hub Models](https://github.com/qualcomm/ai-hub-models/blob/v0.57.1/src/qai_hub_models/models/rangenet_plus_plus) 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 [RangeNet-Plus-Plus on GitHub](https://github.com/qualcomm/ai-hub-models/blob/v0.57.1/src/qai_hub_models/models/rangenet_plus_plus) for usage instructions.
## Model Details
**Model Type:** Model_use_case.driver_assistance
**Model Stats:**
- Model checkpoint: darknet53_rangenet++
- Input resolution: 64x2048
- Input channels: 5
- Number of output classes: 20
- Backbone: DarkNet-53
## Performance Summary
| Model | Runtime | Precision | Chipset | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit
|---|---|---|---|---|---|---
| RangeNet-Plus-Plus | ONNX | float | Snapdragon® X2 Elite | 49.351 ms | 210 - 210 MB | NPU
| RangeNet-Plus-Plus | ONNX | float | Snapdragon® X Elite | 99.787 ms | 147 - 147 MB | NPU
| RangeNet-Plus-Plus | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 75.688 ms | 47 - 492 MB | NPU
| RangeNet-Plus-Plus | ONNX | float | Snapdragon® 8 Gen 1 Mobile | 214.848 ms | 3 - 441 MB | NPU
| RangeNet-Plus-Plus | ONNX | float | Qualcomm® QCS8550 (Proxy) | 102.175 ms | 0 - 113 MB | NPU
| RangeNet-Plus-Plus | ONNX | float | Qualcomm® QCS8450 | 214.848 ms | 3 - 441 MB | NPU
| RangeNet-Plus-Plus | ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 42.224 ms | 2 - 342 MB | NPU
| RangeNet-Plus-Plus | ONNX | float | Qualcomm® QCS9075 | 159.825 ms | 2 - 48 MB | NPU
| RangeNet-Plus-Plus | ONNX | float | Snapdragon® 8 Elite Mobile | 59.183 ms | 1 - 321 MB | NPU
| RangeNet-Plus-Plus | ONNX | float | Qualcomm® QCS8750 | 59.183 ms | 1 - 321 MB | NPU
| RangeNet-Plus-Plus | ONNX | float | Qualcomm® QCS7181 | 99.787 ms | 147 - 147 MB | NPU
| RangeNet-Plus-Plus | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 78.955 ms | 1 - 510 MB | NPU
| RangeNet-Plus-Plus | TFLITE | float | Snapdragon® 8 Gen 1 Mobile | 197.635 ms | 1 - 499 MB | NPU
| RangeNet-Plus-Plus | TFLITE | float | Qualcomm® QCS8275 | 596.038 ms | 1 - 308 MB | NPU
| RangeNet-Plus-Plus | TFLITE | float | Qualcomm® QCS8550 (Proxy) | 106.778 ms | 0 - 96 MB | NPU
| RangeNet-Plus-Plus | TFLITE | float | Qualcomm® SA8775P | 1229.142 ms | 0 - 29 MB | GPU
| RangeNet-Plus-Plus | TFLITE | float | Qualcomm® SA8650P | 1229.142 ms | 0 - 29 MB | GPU
| RangeNet-Plus-Plus | TFLITE | float | Qualcomm® SA8255P | 1229.142 ms | 0 - 29 MB | GPU
| RangeNet-Plus-Plus | TFLITE | float | Qualcomm® QCS8450 | 197.635 ms | 1 - 499 MB | NPU
| RangeNet-Plus-Plus | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 43.272 ms | 6 - 323 MB | NPU
| RangeNet-Plus-Plus | TFLITE | float | Qualcomm® SA7255P | 596.038 ms | 1 - 308 MB | NPU
| RangeNet-Plus-Plus | TFLITE | float | Qualcomm® QCS9075 | 167.687 ms | 0 - 107 MB | NPU
| RangeNet-Plus-Plus | TFLITE | float | Snapdragon® 8 Elite Mobile | 60.072 ms | 1 - 294 MB | NPU
| RangeNet-Plus-Plus | TFLITE | float | Qualcomm® SA8295P | 172.041 ms | 1 - 302 MB | NPU
| RangeNet-Plus-Plus | TFLITE | float | Qualcomm® QCS8750 | 60.072 ms | 1 - 294 MB | NPU
## License
* The license for the original implementation of RangeNet-Plus-Plus can be found
[here](https://github.com/PRBonn/lidar-bonnetal/blob/master/LICENSE).
## References
* [RangeNet++: Fast and Accurate LiDAR Semantic Segmentation](https://ieeexplore.ieee.org/document/8967762)
* [Source Model Implementation](https://github.com/PRBonn/lidar-bonnetal)
## Community
* Join [our AI Hub Slack community](https://aihub.qualcomm.com/community/slack) to collaborate, post questions and learn more about on-device AI.
* For questions or feedback please [reach out to us](mailto:ai-hub-support@qti.qualcomm.com).
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