https://github.com/fixstars/cuda-efficient-features
A CUDA implementation of keypoint detection and descriptor extraction
https://github.com/fixstars/cuda-efficient-features
computer-vision cuda descriptors local-features robotics slam structure-from-motion
Last synced: about 1 year ago
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A CUDA implementation of keypoint detection and descriptor extraction
- Host: GitHub
- URL: https://github.com/fixstars/cuda-efficient-features
- Owner: fixstars
- License: apache-2.0
- Created: 2023-11-05T09:21:13.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-01-22T06:14:22.000Z (over 2 years ago)
- Last Synced: 2025-04-13T07:16:25.696Z (about 1 year ago)
- Topics: computer-vision, cuda, descriptors, local-features, robotics, slam, structure-from-motion
- Language: C
- Homepage:
- Size: 453 KB
- Stars: 80
- Watchers: 3
- Forks: 9
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# cuda-efficient-features
A CUDA implementation of keypoint detection and descriptor extraction
---
## Description
This project implements keypoint detection and descriptor extraction in CUDA,
motivated by [iago-suarez/efficient-descriptors](https://github.com/iago-suarez/efficient-descriptors) and provides the following features.
### Keypoint Detection
- Provides CUDA implementation of multi-scale FAST corner detection based on OpenCV's `cv::ORB::detect`
- Added functionality for controlling distribution of keypoints
The figure below shows a comparison of keypoint detection results between OpenCV's implementation and this project's one.
**Left**: Even though OpenCV's implementation detected about 20,000 keypoints, most of them were concentrated in the leaves of trees.
**Right**: With this project's `nonmaxRadius` setting of 15, the keypoints are detected evenly throughout the image.
### Descriptor Extraction
- Provides CUDA implementation of **BAD** and **HashSIFT** descriptors proposed by Suarez et al. [1][2]
### OpenCV API
- Provides APIs in the same format as OpenCV's `cv::Feature2D`
- `detect,compute,detectAndCompute`
- Also provides asynchronous APIs for concurrent execution
- `detectAsync,computeAsync,detectAndComputeAsync`
---
## References
- [1] Suarez, Iago, Jose M. Buenaposada, and Luis Baumela. "Revisiting binary local image description for resource limited devices." IEEE Robotics and Automation Letters 6.4 (2021): 8317-8324.
- [2] https://github.com/iago-suarez/efficient-descriptors
---
## Performance
Using `sample_benchmark`, We measured the processing time for each API below.
- `detect`: keypoint detection only
- `compute`: descriptor extraction only
- `detectAndCompute`: keypoint detection and descriptor extraction
Each processing time below is an average of 11 images obtained from [SceauxCastle](https://github.com/openMVG/ImageDataset_SceauxCastle). The unit of time is milliseconds.
### detect
With the default parameters, we measured the processing time of keypoint detection while changing the image size to FHD(1920x1080), 4K(3840x2160), and 8K(7680x4320).
| Device | FHD | 4K | 8K |
|---------------|-----|------|------|
| RTX 3060 Ti | 1.6 | 2.9 | 5.5 |
| Jetson Xavier | 5.6 | 12.1 | 27.5 |
### compute
For each descriptor, we measured the processing time of descriptor extraction for 40,000 keypoints.
| Device | BAD256 | BAD512 | HashSIFT256 | HashSIFT512 |
|---------------|--------|--------|-------------|-------------|
| RTX 3060 Ti | 1.5 | 2.7 | 3.5 | 3.9 |
| Jetson Xavier | 19.1 | 28.2 | 21.9 | 24.8 |
### detectAndCompute
For each descriptor, we measured the processing time when executing both keypoint detection and descriptor extraction for 40,000 keypoints.
| Device | BAD256 | BAD512 | HashSIFT256 | HashSIFT512 |
|---------------|--------|--------|-------------|-------------|
| RTX 3060 Ti | 7.2 | 8.2 | 8.5 | 8.9 |
| Jetson Xavier | 41.7 | 48.8 | 46.2 | 49.2 |
---
## Requirements
|Package Name|Minimum Requirements|Note|
|---|---|---|
|CMake|version >= 3.18||
|CUDA Toolkit|compute capability >= 6.0|
|OpenCV|version >= 4.6.0||
|OpenCV CUDA module|version >= 4.6.0|included in [opencv/opencv_contrib](https://github.com/opencv/opencv_contrib)|
---
## How to build
```
$ git clone https://github.com/fixstars/cuda-efficient-features.git
$ cd cuda-efficient-features
$ git submodule update --init # needed if BUILD_TESTS is ON
$ mkdir build
$ cd build
$ cmake ../ # Several options available (e.g. -DBUILD_TESTS=ON -DCUDA_ARCHS=86)
$ make
```
### CMake options
|Option|Description|Default|
|---|---|---|
|BUILD_SAMPLES|Build samples|`ON`|
|BUILD_TESTS|Build tests|`OFF`|
|CUDA_ARCHS|List of architectures to generate device code for|`52;61;72;75;86`|
## How to run
### `samples`
|Command|Description|
|---|---|
|`./samples/sample_feature_extraction input-image [options]`|Feature detection and description|
|`./samples/sample_feature_matching first-image second-image [options]`|Feature matching on an image pair|
|`./samples/sample_image_sequence image-format [options]`|Feature matching on an image sequence|
|`./samples/sample_benchmark input-image [options]`|Performance benchmarking|
|`./samples/hpatches_description hpatchs-dir [options]`|Feature description on HPatches dataset
for [hpatches-benchmark](https://github.com/hpatches/hpatches-benchmark)|
Use the `--help` or `-h` option for detailed information.
```
./samples/sample_feature_extraction -h
```
### `tests`
Run the following command.
```
./tests/tests
```
---
## Author
The "adaskit Team"
The adaskit is an open-source project created by [Fixstars Corporation](https://www.fixstars.com/) and its subsidiary companies including [Fixstars Autonomous Technologies](https://at.fixstars.com/), aimed at contributing to the ADAS industry by developing high-performance implementations for algorithms with high computational cost.
---
## License
Apache License 2.0