---
library_name: pytorch
license: other
tags:
- android
pipeline_tag: other

---

![](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/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 2.43 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/centerpoint/releases/v0.48.0/centerpoint-qnn_dlc-float.zip)
| TFLITE | float | Universal | TFLite 2.17.0 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/centerpoint/releases/v0.48.0/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/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/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 | 180.87 ms | 2 - 2 MB | NPU
| CenterPoint | QNN_DLC | float | Snapdragon® X Elite | 312.052 ms | 2 - 2 MB | NPU
| CenterPoint | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 244.881 ms | 0 - 752 MB | NPU
| CenterPoint | QNN_DLC | float | Qualcomm® QCS8275 (Proxy) | 909.147 ms | 1 - 452 MB | NPU
| CenterPoint | QNN_DLC | float | Qualcomm® QCS8550 (Proxy) | 323.63 ms | 2 - 366 MB | NPU
| CenterPoint | QNN_DLC | float | Qualcomm® QCS9075 | 397.079 ms | 2 - 11 MB | NPU
| CenterPoint | QNN_DLC | float | Qualcomm® QCS8450 (Proxy) | 510.792 ms | 1 - 1067 MB | NPU
| CenterPoint | QNN_DLC | float | Snapdragon® 8 Elite For Galaxy Mobile | 201.763 ms | 0 - 449 MB | NPU
| CenterPoint | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 171.022 ms | 2 - 444 MB | NPU
| CenterPoint | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 3953.577 ms | 2652 - 2660 MB | CPU
| CenterPoint | TFLITE | float | Qualcomm® QCS8275 (Proxy) | 6321.638 ms | 2607 - 2615 MB | CPU
| CenterPoint | TFLITE | float | Qualcomm® QCS8550 (Proxy) | 5057.038 ms | 2568 - 2595 MB | CPU
| CenterPoint | TFLITE | float | Qualcomm® QCS8450 (Proxy) | 5831.27 ms | 2625 - 2635 MB | CPU
| CenterPoint | TFLITE | float | Snapdragon® 8 Elite For Galaxy Mobile | 3022.673 ms | 2620 - 2629 MB | CPU
| CenterPoint | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 2542.905 ms | 2652 - 2662 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).