--- title: CenterPoint canonical_url: "https://www.modelscope.cn/models/qualcomm/CenterPoint" md_url: "https://www.modelscope.cn/models/qualcomm/CenterPoint.md" repository: qualcomm/CenterPoint last_updated: 2026-06-16 license: other library_name: - pytorch frameworks: - pytorch downloads: 36 stars: 0 tags: - android --- # CenterPoint > CenterPoint - qualcomm 在 ModelScope 开源的模型。CenterPoint: Optimized for Qualcomm Devices qualcomm/CenterPoint 是 ModelScope 魔搭社区上的机器学习模型，采用 other 许可。 - **Repository**: qualcomm/CenterPoint - **License**: other - **Tags**: android - **Downloads**: 36 - **Stars**: 0 - **Last updated**: 2026-06-16 Source: https://www.modelscope.cn/models/qualcomm/CenterPoint --- ![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/centerpoint/web-assets/model_demo.png) # CenterPoint: Optimized for Qualcomm Devices CenterPoint is a LiDAR-based 3D object detection model that detects objects by predicting their centers and regressing other attributes. It is designed for high accuracy and real-time performance in autonomous driving applications. 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/centerpoint) 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 | |---|---|---|---|---| | QNN_DLC | float | Universal | QAIRT 2.45 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/centerpoint/releases/v0.56.0/centerpoint-qnn_dlc-float.zip) | TFLITE | float | Universal | | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/centerpoint/releases/v0.56.0/centerpoint-tflite-float.zip) For more device-specific assets and performance metrics, visit **[CenterPoint on Qualcomm® AI Hub](https://aihub.qualcomm.com/models/centerpoint)**. ### 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/centerpoint) 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 [CenterPoint on GitHub](https://github.com/qualcomm/ai-hub-models/blob/main/src/qai_hub_models/models/centerpoint) for usage instructions. ## Model Details **Model Type:** Model_use_case.driver_assistance **Model Stats:** - Model checkpoint: PointPillars - Input resolution: 5x20x5, 5x4, 5 - Number of parameters: 21.8M - Model size: 83.3 MB ## Performance Summary | Model | Runtime | Precision | Chipset | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit |---|---|---|---|---|---|--- | CenterPoint | QNN_DLC | float | Snapdragon® X2 Elite | 182.793 ms | 2 - 2 MB | NPU | CenterPoint | QNN_DLC | float | Snapdragon® X Elite | 322.157 ms | 2 - 2 MB | NPU | CenterPoint | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 252.712 ms | 0 - 753 MB | NPU | CenterPoint | QNN_DLC | float | Snapdragon® 8 Gen 1 Mobile | 518.366 ms | 2 - 736 MB | NPU | CenterPoint | QNN_DLC | float | Qualcomm® QCS8275 | 920.139 ms | 2 - 451 MB | NPU | CenterPoint | QNN_DLC | float | Qualcomm® QCS8550 (Proxy) | 335.833 ms | 2 - 4 MB | NPU | CenterPoint | QNN_DLC | float | Qualcomm® QCS8450 | 518.366 ms | 2 - 736 MB | NPU | CenterPoint | QNN_DLC | float | Snapdragon® 8 Elite Mobile | 210.78 ms | 0 - 461 MB | NPU | CenterPoint | QNN_DLC | float | Qualcomm® SA8295P | 442.021 ms | 1 - 449 MB | NPU | CenterPoint | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 174.671 ms | 2 - 722 MB | NPU | CenterPoint | QNN_DLC | float | Qualcomm® SA7255P | 920.139 ms | 2 - 451 MB | NPU | CenterPoint | QNN_DLC | float | Qualcomm® QCS9075 | 423.202 ms | 2 - 11 MB | NPU | CenterPoint | QNN_DLC | float | Qualcomm® QCS8750 | 210.78 ms | 0 - 461 MB | NPU | CenterPoint | QNN_DLC | float | Qualcomm® QCS7181 | 322.157 ms | 2 - 2 MB | NPU | CenterPoint | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 3854.229 ms | 1835 - 1843 MB | CPU | CenterPoint | TFLITE | float | Snapdragon® 8 Gen 1 Mobile | 5099.219 ms | 1858 - 1870 MB | CPU | CenterPoint | TFLITE | float | Qualcomm® QCS8275 | 6174.644 ms | 1848 - 1856 MB | CPU | CenterPoint | TFLITE | float | Qualcomm® QCS8550 (Proxy) | 5389.073 ms | 1828 - 1836 MB | CPU | CenterPoint | TFLITE | float | Qualcomm® SA8775P | 5380.349 ms | 1807 - 1813 MB | CPU | CenterPoint | TFLITE | float | Qualcomm® SA8650P | 5380.349 ms | 1807 - 1813 MB | CPU | CenterPoint | TFLITE | float | Qualcomm® SA8255P | 5380.349 ms | 1807 - 1813 MB | CPU | CenterPoint | TFLITE | float | Qualcomm® QCS8450 | 5099.219 ms | 1858 - 1870 MB | CPU | CenterPoint | TFLITE | float | Snapdragon® 8 Elite Mobile | 2816.604 ms | 1853 - 1865 MB | CPU | CenterPoint | TFLITE | float | Qualcomm® SA8295P | 3502.138 ms | 1807 - 1813 MB | CPU | CenterPoint | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 805.643 ms | 1979 - 1988 MB | CPU | CenterPoint | TFLITE | float | Qualcomm® SA7255P | 6174.644 ms | 1848 - 1856 MB | CPU | CenterPoint | TFLITE | float | Qualcomm® QCS9075 | 5160.994 ms | 2362 - 2383 MB | CPU | CenterPoint | TFLITE | float | Qualcomm® QCS8750 | 2816.604 ms | 1853 - 1865 MB | CPU ## License * The license for the original implementation of CenterPoint can be found [here](https://github.com/tianweiy/CenterPoint/blob/master/LICENSE). ## 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).