--- library_name: pytorch license: other tags: - real_time - bu_auto - android pipeline_tag: other --- ![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/rangenet_plus_plus/web-assets/model_demo.png) # 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/main/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 2.42, ONNX Runtime 1.24.3 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/rangenet_plus_plus/releases/v0.53.1/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/v0.53.1/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/main/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/main/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® 8 Elite Gen 5 Mobile | 41.39 ms | 3 - 335 MB | NPU | RangeNet-Plus-Plus | ONNX | float | Snapdragon® 8 Elite Mobile | 58.534 ms | 0 - 329 MB | NPU | RangeNet-Plus-Plus | ONNX | float | Snapdragon® X2 Elite | 49.501 ms | 101 - 101 MB | NPU | RangeNet-Plus-Plus | ONNX | float | Snapdragon® X Elite | 100.677 ms | 100 - 100 MB | NPU | RangeNet-Plus-Plus | ONNX | float | Snapdragon® X Elite | 100.677 ms | 100 - 100 MB | NPU | RangeNet-Plus-Plus | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 74.569 ms | 0 - 457 MB | NPU | RangeNet-Plus-Plus | ONNX | float | Qualcomm® QCS8550 (Proxy) | 102.668 ms | 3 - 5 MB | NPU | RangeNet-Plus-Plus | ONNX | float | Qualcomm® QCS9075 | 159.07 ms | 2 - 8 MB | NPU | RangeNet-Plus-Plus | ONNX | float | Snapdragon® 8 Elite For Galaxy Mobile | 58.534 ms | 0 - 329 MB | NPU | RangeNet-Plus-Plus | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 44.421 ms | 0 - 315 MB | NPU | RangeNet-Plus-Plus | TFLITE | float | Snapdragon® 8 Elite Mobile | 60.235 ms | 0 - 296 MB | NPU | RangeNet-Plus-Plus | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 78.37 ms | 0 - 511 MB | NPU | RangeNet-Plus-Plus | TFLITE | float | Qualcomm® QCS8275 (Proxy) | 595.836 ms | 0 - 308 MB | NPU | RangeNet-Plus-Plus | TFLITE | float | Qualcomm® QCS8550 (Proxy) | 97.484 ms | 0 - 96 MB | NPU | RangeNet-Plus-Plus | TFLITE | float | Qualcomm® SA8775P | 154.76 ms | 0 - 308 MB | NPU | RangeNet-Plus-Plus | TFLITE | float | Qualcomm® SA8775P | 154.76 ms | 0 - 308 MB | NPU | RangeNet-Plus-Plus | TFLITE | float | Qualcomm® SA8775P | 154.76 ms | 0 - 308 MB | NPU | RangeNet-Plus-Plus | TFLITE | float | Qualcomm® QCS9075 | 167.36 ms | 0 - 107 MB | NPU | RangeNet-Plus-Plus | TFLITE | float | Qualcomm® QCS8450 (Proxy) | 195.519 ms | 1 - 500 MB | NPU | RangeNet-Plus-Plus | TFLITE | float | Qualcomm® SA7255P | 595.836 ms | 0 - 308 MB | NPU | RangeNet-Plus-Plus | TFLITE | float | Qualcomm® SA8295P | 171.967 ms | 0 - 302 MB | NPU | RangeNet-Plus-Plus | TFLITE | float | Snapdragon® 8 Elite For Galaxy Mobile | 60.235 ms | 0 - 296 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).