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Image stitching technology competition: which is stronger, SFM or SLAM?


Image stitching is the process of combining multiple overlapping images from different viewpoints into a single seamless image. In simple terms, it involves finding common feature points in two images and performing the stitching action. It is commonly used in photography and computer vision to create panoramic images or image mosaics.


image stitching
Image Stitching, source: https://commons.wikimedia.org/wiki/File:Rochester_NY.jpg
Drones open up a new "view" of the world, and the new millennium eye of the 21st century is born.

With the rapid development of drone technology in recent years, aerial photography is a way to collect information from a distance. Its application in agricultural planning is increasingly widespread, helping us effectively improve land use efficiency and provide assistance in rescue operations in the event of emergencies. Through aerial data, we can also gain a deeper understanding of the impact of events such as oil spills, landslides, flood threats, and tsunamis on soil and water. Some people view drone aerial photography as a science, while others consider it a technology. Regardless of how it is viewed, it is a practical tool that helps us better understand the world and make more informed decisions.


Is image stitching complicated?

Image stitching involves multiple layers, including image calibration (aligning the photos to be stitched) and image blending (combining the photos into a single seamless image). Image calibration is typically done using techniques such as feature matching, which involves identifying and matching corresponding points in different photos. Image blending can be performed using various techniques such as averaging or feathering, which smooth out transitions between images. Common stitching tools like the paid version of Photoshop software require extremely complex manual modes to combine individual photos into a single image, as demonstrated in the following video.



SfM vs. SLAM


Structure from Motion (SfM)

Structure from Motion(SFM)is a technique used in computer vision for creating 3D models or maps of the environment from multiple 2D images. It works by analyzing the motion of the cameras that took the images and estimating the 3D structure of the scene.

SFM is commonly used in applications such as creating 3D models of buildings or landscapes from photographs, generating 3D point clouds for cultural heritage applications, or creating 3D models for virtual reality or augmented reality applications. It can be implemented using a variety of algorithms and software packages, and it typically requires a set of images taken from different viewpoints of the same scene.


SfM
SfM, source: https://cvg.ethz.ch/.../privacy.../images/teaser.png?fbclid=IwAR22zrVvvl-mL6LQQMsQIJD6r3Ta2gseKTQy_LmyNYNXBdek5K11vNORs64
DSM
DSM, credit to Kbosak from https://commons.wikimedia.org/wiki/File:DSM_construction_site.jpg









Simultaneous localization and mapping (SLAM)

In photogrammetry, SLAM (Simultaneous Localization and Mapping) is a technique that can be used to create a 3D map of an environment from a series of 2D images. It works by analyzing the motion of the camera that took the images and simultaneously determining the position of the camera within the 3D map. SLAM algorithms can be used to create 3D maps from images taken by a moving camera, such as a drone or a hand-held camera. They can also be used to create 3D maps from images taken by a stationary camera, such as a camera mounted on a tripod or a camera mounted on a building or a bridge. In photogrammetry, SLAM can be used in various applications, such as creating 3D models of buildings or landscapes, generating 3D point clouds for cultural heritage applications, or creating 3D models for virtual output depends or augmented reality applications. It can be implemented using a variety of algorithms and software packages, and it typically requires a set of images taken from different viewpoints of the same scene.


SLAM for robotics
SLAM was originally used as a robot, and the picture shows the maze drawn by the robot using SLAM.

Comparison

​SfM

SLAM

Data Source

SfM needs a set of images taken from different viewpoints of the same scene

SLAM can use sensors such as cameras, lasers, or radar that can gather data about the environment

Output

A DOM, 3D model or point cloud of the scene, used for creating 3D models from static scenes

An orthophoto, a 3D map of the environment and the location of the camera within that map is suited for dynamic environments and real-time tracking.

Speed

Depends on the number and quality of the input images and the complexity of the scene. It can be relatively slow for large and complex scenes.

Depends on the number and quality of the input images, and is typically faster than SFM for real-time tracking and navigation.

Accuracy

It is generally more accurate for creating 3D models from static scenes.

It is generally less accurate than SFM for creating 3D models, but it is better suited for real-time tracking and navigation in dynamic environments.


SfM vs SLAM
Source: https://www.researchgate.net/figure/Similar-pipeline-of-SfM-1-and-SLAM-2-algorithms_fig1_337119004

Overall, the choice of which technique to use for image stitching will depend on the decision maker's specific requirements and environmental constraints. Both SFM and SLAM provide powerful features such as orthophoto images, but SLAM has some additional advantages that make it particularly suitable for certain application scenarios.


1. Real-time operation:

As mentioned previously, one of the main advantages of SLAM is its ability to operate in real-time. This means that a drone can continuously update its position and orientation as it moves through the environment, resulting in a more natural and smooth final image. This is especially useful in dynamic scenes or situations where the drone must frequently change its location, such as search and rescue operations or environmental monitoring.


2. Improving efficiency:

SLAM technology can greatly improve the speed of image stitching through the process of automatically capturing and aligning photos. This can save time and effort for the operator and allow for larger or more complex scenes to be captured with minimal effort.


In short, as the capabilities of SLAM algorithms continue to improve and become more prevalent, their real-time operation and higher efficiency make them increasingly attractive for use in image stitching, with applications including robot navigation, AR, and drone aerial photography. We can expect to see the use of this technology in the field of image stitching become more widespread.


 

Don't know how to do it? Let us help you!

DataXquad is a Pay-Per-Use online image data transformation service platform that can meet all procedures without use costs. We simplify the most complicated part of image data transformation so that more image data can be utilized and more industry chains can derive value from it.


For image stitching, we offer two customized services: one is SFM, and the other is SLAM. Each has its advantages and disadvantages, and the choice of which technology to use depends on the decision maker's specific requirements and environmental constraints.



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