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LightGlue-OnnxRunner

Introduction

LightGlue-OnnxRunner is a repository hosts the C++ inference code of LightGlue in ONNX format. LightGlue is a lightweight feature matcher with high accuracy and blazing fast inference. It takes as input a set of keypoints and descriptors for each image and returns the indices of corresponding points.

superpoint_lightglue_end2end效果图

superpoint_lightglue_end2end.onnx renderings

disk_lightglue_end2end效果图

disk_lightglue_end2end.onnx renderings

Attention⚠️

Currently, the interface only supports CPU execution.The specific experimental data and equipment used are shown below. And the inferface is only supported on Windows and may encounter issues when running on Linux.

Updates📰

  • [2023.09.08] : LightGlueOnnxRunner supporting end-to-end model inference of SuperPoint and DISK
  • [2023.09.11] : LightGlueDecoupleOnnxRunner supporting decouple model inference of SuperPoint/DISK + LightGlue
  • [2023.09.12] : Support calling GPU inference and complete README.md experimental data

Development Enviroments🖥️

  • Windows 11 Professional
  • CUDA v11.7
  • cmake version 3.26.2

Quick Start

Installation

Install this repo in the following ways :

git clone https://github.com/OroChippw/LightGlue-OnnxRunner.git
cd LightGlue-OnnxRunner

Requirements⚒️

# onnxruntime-cpu 3rdparty
This repository use onnxruntime-win-x64-1.14.1
# onnxruntime-gpu 3rdparty
This repository use onnxruntime-win-x64-gpu-1.15.0 # for CUDA 11.7
# opencv 3rdparty
This repository use opencv4.8.0
# CXX_STANDARD 17

Build and Run

# Enter the source code directory where CMakeLists.txt is located, and create a new build folder
mkdir build
# Enter the build folder and run CMake to configure the project
cd build
cmake ..
# Use the build system to compile/link this project
cmake --build .
# If the specified compilation mode is debug or release, it is as follows
# cmake --build . --config Debug
# cmkae --build . --config Release

Model Checkpoints

Experiment Record

Environment Device : i5-13500H + NVIDIA GeForce RTX 4060 Laptop GPU（8GB）.
All models are available in repository fabio-sim/LightGlue-ONNX
The inference speed of onnxruntime-GPU will be slower when the first image is loaded, and will return to normal speed later.

Decouple

Extractor Type Extractor Model Name CPU speed(ms) GPU speed(ms) Matcher Model Name CPU speed(ms) GPU speed(ms)
SuperPoint superpoint.onnx Debug:123ms Release: 73ms Debug:15ms Release:13ms superpoint_lightglue.onnx Debug:2384ms Release: 2112ms Debug:155ms Release:230ms
DISK disk.onnx Debug:341ms Release: 336ms Debug:28ms Release:25ms disk_lightglue.onnx Debug:3347ms Release: 3257ms Debug: 230ms Release:245ms

End-to-End🌟🌟🌟

Extractor Type Model Name Model Size(MB/GB) CPU speed(ms) GPU speed(ms)
SuperPoint superpoint_lightglue_end2end.onnx 50.1MB Debug:2181ms Release: 1829ms Debug: 170ms Release:166ms
DISK disk_lightglue_end2end.onnx 48.9MB Debug:3312ms Release: 3287ms Debug: 285ms Release:285ms

CHANGELOG

20231120

  • 适配v1.0.0: Fused LightGlue-ONNX接口改动

  • 适配linux平台，完成CPU下测试，移除部分Linux下用不到的代码

  • 修复可视化时，两张输入图片大小不一致时匹配特征计算错误导致的特征显示错误

  • 非end-to-end模式增加sp和lg的独立接口

License

This project is licensed under the MIT License.