This is the fourth version of a SIFT (Scale Invariant Feature Transform) implementation using CUDA for GPUs from NVidia. The first version is from 2007 and GPUs have evolved since then. This version is slightly more precise and considerably faster than the previous versions and has been optimized for Kepler and later generations of GPUs.
On a GTX 1060 GPU the code takes about 2.7 ms on a 1280x960 pixel image and 3.8 ms on a 1920x1080 pixel image. There is also code for brute-force matching of features and homography computation that takes about 3.7 ms for two sets of around 2250 SIFT features each.
The code relies on CMake for compilation and OpenCV for image containers. OpenCV can however be quite easily changed to something else. The code can be relatively hard to read, given the way things have been parallelized for maximum speed.
The code is free to use for non-commercial applications. If you use the code for research, please refer to the following paper.
M. Björkman, N. Bergström and D. Kragic, "Detecting, segmenting and tracking unknown objects using multi-label MRF inference", CVIU, 118, pp. 111-127, January 2014. ScienceDirect
Computational cost (in milliseconds) on different GPUs (latest benchmark marked with *):
| 1280x960 | 1920x1080 | GFLOPS | Bandwidth | Matching | ||
|---|---|---|---|---|---|---|
| Pascal | GeForce GTX 1080 Ti | 1.7* | 2.3* | 10609 | 484 | 1.4* |
| Pascal | GeForce GTX 1060 | 2.7* | 4.0* | 3855 | 192 | 2.6* |
| Maxwell | GeForce GTX 970 | 3.8* | 5.6* | 3494 | 224 | 2.8* |
| Kepler | Tesla K40c | 5.4* | 8.0* | 4291 | 288 | 5.5* |
| Kepler | GeForce GTX TITAN | 4.4* | 6.6* | 4500 | 288 | 4.6* |
Matching is done between two sets of 1616 and 1769 features respectively.
The latest improvements involve a slight adaptation for Pascal, changing from textures to global memory (mostly through L2) in the most costly function LaplaceMulti. The new medium-end card GTX 1060 is impressive indeed. It will be interesting to see the performance on the NVidia Titan X and other Pascal cards.
There are two different containers for storing data on the host and on the device; SiftData for SIFT features and CudaImage for images. Since memory allocation on GPUs is slow, it's usually preferable to preallocate a sufficient amount of memory using InitSiftData(), in particular if SIFT features are extracted from a continuous stream of video camera images. On repeated calls ExtractSift() will reuse memory previously allocated.
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <cudaImage.h>
#include <cudaSift.h>
/* Reserve memory space for a whole bunch of SIFT features. */
SiftData siftData;
InitSiftData(siftData, 25000, true, true);
/* Read image using OpenCV and convert to floating point. */
cv::Mat limg;
cv::imread("image.png", 0).convertTo(limg, CV32FC1);
/* Allocate 1280x960 pixel image with device side pitch of 1280 floats. */
/* Memory on host side already allocated by OpenCV is reused. */
CudaImage img;
img.Allocate(1280, 960, 1280, false, NULL, (float*) limg.data);
/* Download image from host to device */
img.Download();
int numOctaves = 5; /* Number of octaves in Gaussian pyramid */
float initBlur = 1.0f; /* Amount of initial Gaussian blurring in standard deviations */
float thresh = 3.5f; /* Threshold on difference of Gaussians for feature pruning */
float minScale = 0.0f; /* Minimum acceptable scale to remove fine-scale features */
bool upScale = false; /* Whether to upscale image before extraction */
/* Extract SIFT features */
ExtractSift(siftData, img, numOctaves, initBlur, thresh, minScale, upScale);
...
/* Free space allocated from SIFT features */
FreeSiftData(siftData);The requirements on number and quality of features vary from application to application. Some applications benefit from a smaller number of high quality features, while others require as many features as possible. More distinct features with higher DoG (difference of Gaussians) responses tend to be of higher quality and are easier to match between multiple views. With the parameter thresh a threshold can be set on the minimum DoG to prune features of less quality.
In many cases the most fine-scale features are of little use, especially when noise conditions are severe or when features are matched between very different views. In such cases the most fine-scale features can be pruned by setting minScale to the minimum acceptable feature scale, where 1.0 corresponds to the original image scale without upscaling. As a consequence of pruning the computational cost can also be reduced.
To increase the number of SIFT features, but also increase the computational cost, the original image can be automatically upscaled to double the size using the upScale parameter, in accordings with Lowe's recommendations. One should keep in mind though that by doing so the fraction of features that can be matched tend to go down, even if the total number of extracted features increases significantly. If it's enough to instead reduce the thresh parameter to get more features, that is often a better alternative.
Results without upscaling (upScale=False) of 1280x960 pixel input image.
| thresh | #Matches | %Matches | Cost (ms) |
|---|---|---|---|
| 1.0 | 4236 | 40.4% | 5.8 |
| 1.5 | 3491 | 42.5% | 5.2 |
| 2.0 | 2720 | 43.2% | 4.7 |
| 2.5 | 2121 | 44.4% | 4.2 |
| 3.0 | 1627 | 45.8% | 3.9 |
| 3.5 | 1189 | 46.2% | 3.6 |
| 4.0 | 881 | 48.5% | 3.3 |
Results with upscaling (upScale=True) of 1280x960 pixel input image.
| thresh | #Matches | %Matches | Cost (ms) |
|---|---|---|---|
| 2.0 | 4502 | 34.9% | 13.2 |
| 2.5 | 3389 | 35.9% | 11.2 |
| 3.0 | 2529 | 37.1% | 10.6 |
| 3.5 | 1841 | 38.3% | 9.9 |
| 4.0 | 1331 | 39.8% | 9.5 |
| 4.5 | 954 | 42.2% | 9.3 |
| 5.0 | 611 | 39.3% | 9.1 |