AXERA-TECH/centerpoint

By AXERA-TECH · ↓ 7 · ♥ 0 · Updated 2026-01-28

What's in this repo

This page lists the files in AXERA-TECH/centerpoint, including pointpillars.onnx.

1 ONNX models (.onnx files)
112 files, including 53 .json, 50 .bin, 2 .py, 2 .gz, 1 .md, 1 .axmodel
Last updated 2026-01-28

ONNX files

File	Size	Download
pointpillars.onnx	—	Download

How to use

These ONNX files can be loaded with ONNX Runtime. To inspect a model graph, upload the file to the Netron viewer. Download links point to the original files on Hugging Face.

README

CenterPoint on Axera NPU

This repository contains the CenterPoint model converted for high-performance inference on the Axera NPU. CenterPoint is a center-based framework for 3D object detection and tracking that represents objects as points, significantly simplifying the detection pipeline on LiDAR point clouds.

This version is optimized with w8a16 quantization and is compatible with Pulsar2 version 4.2.

Convert Tools Links

For model conversion and deployment guidance:

AXera Platform GitHub Repo: Sample code and optimization guides for Axera NPU.
Pulsar2 Documentation: Guide for converting ONNX models to .axmodel.

Support Platforms

AX650
- M4N-Dock (爱芯派Pro)
- M.2 Accelerator card

Chips	Model Variant	NPU3 Latency (ms)
AX650	CenterPoint-Pillar	88.334

How to Use

Download the repository and ensure the directory structure is organized as follows:

.
├── centerpoint.axmodel       # The compiled Axera model
├── inference_axmodel.py      # Main inference script
└── extracted_data/           # Input directory
    ├── config.json           # Configuration files (e.g., inference_config.json)
    ├── sample_index.json
    ├── gt_annotations/
    └── points/

Prerequisites

Environment: Ensure you have the required Python environment activated with the following core packages installed:
- NPU Runtime: axengine (PyAXEngine)
- Core Libraries: numba , opencv-python and tqdm.
Model/Data: Ensure the compiled .axmodel, inference_config.json, and input data (inference_data/) are available on the host.

Inference Command

Run the inference script by providing the compiled model, configuration, and data directory.

python inference_axmodel.py ./centerpoint.axmodel ./extracted_data/config.json ./extracted_data --output-dir ./inference_results --visualize --num-samples 50 --score-thr 0.5

Inference with AX650 Host

Results

The model generates a 3D detection map with bounding boxes oriented in 3D space. Results are saved as images and videos which visualize the ego-vehicle, point cloud data, and detected objects.

(ax_env) root@ax650:~/data# python inference_axmodel.py ./centerpoint.axmodel ./extracted_data/config.json ./extracted_data --output-dir ./inference_results --visualize --num-samples 50 --score-thr 0.5
[INFO] Available providers:  ['AxEngineExecutionProvider']
[INFO] Using provider: AxEngineExecutionProvider
[INFO] Chip type: ChipType.MC50
[INFO] VNPU type: VNPUType.DISABLED
[INFO] Engine version: 2.12.0s
[INFO] Model type: 2 (triple core)
[INFO] Compiler version: 5.1-patch1 ed388aa0
Processing 50 samples...
Inference: 100%|████████████████████████████████████████████████████████████████████████| 50/50 [00:47<00:00,  1.06it/s]
Creating video: 100%|███████████████████████████████████████████████████████████████████| 50/50 [00:02<00:00, 23.32it/s]
Done! 50 frames, 12836 detections, saved to ./inference_results

Example Visualization

CenterPoint Detection Result GIF

Model info

object-detection

onnx ONNX Axera NPU Pulsar2 CenterPoint 3D-Object-Detection LiDAR object-detection en arxiv:2006.11275 license:mit

↓ 7 ♥ 0

Created: 2026-01-28

Updated: 2026-01-28

View on Hugging Face

Files (112)

.gitattributes

README.md

ax650/centerpoint.axmodel

config.json

extracted_data/config.json

extracted_data/gt_annotations/n008-2018-08-01-15-16-36-0400__LIDAR_TOP__1533151603547590.json

extracted_data/gt_annotations/n008-2018-08-01-15-16-36-0400__LIDAR_TOP__1533151604048025.json

extracted_data/gt_annotations/n008-2018-08-01-15-16-36-0400__LIDAR_TOP__1533151604547893.json

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extracted_data/gt_annotations/n008-2018-08-01-15-16-36-0400__LIDAR_TOP__1533151605548192.json

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extracted_data/gt_annotations/n008-2018-08-27-11-48-51-0400__LIDAR_TOP__1535385092150099.json

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extracted_data/points/n008-2018-08-01-15-16-36-0400__LIDAR_TOP__1533151603547590.bin

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extracted_data/points/n008-2018-08-27-11-48-51-0400__LIDAR_TOP__1535385096399680.bin

extracted_data/sample_index.json

inference_axmodel.py

inference_onnx.py

output.gif

pointpillars.onnx ONNX

quant_data/features.tar.gz

quant_data/indices.tar.gz

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