How can you accurately align photogrammetry data in post-processing?

Photogrammetry is a technique that uses multiple images of an object or a scene to create a 3D model. It can be used for various applications, such as digital preservation, cultural heritage, gaming, and visual effects. However, photogrammetry data often needs to be aligned in post-processing to ensure accuracy, consistency, and quality. In this article, you will learn how to align photogrammetry data in post-processing using some common tools and methods.

1 Why align photogrammetry data?

Alignment is the process of adjusting the position, orientation, and scale of the 3D model and the camera parameters to match the real-world coordinates and measurements. Alignment is important for several reasons. First, it can improve the accuracy and realism of the 3D model by reducing errors and distortions caused by camera calibration, lens distortion, or image alignment. Second, it can enable the integration of the 3D model with other data sources, such as maps, GPS, or laser scans. Third, it can facilitate the editing, manipulation, and export of the 3D model for different purposes and platforms.

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2 How to align photogrammetry data in post-processing?

When aligning photogrammetry data in post-processing, there are various tools and methods to consider, depending on the type and complexity of the data, the desired output, and the available resources. For instance, control points can be used to manually or automatically align the 3D model by matching them with their corresponding locations in the images. Agisoft Metashape, RealityCapture, or Meshroom are some examples of software that support control points. Alternatively, reference objects such as cubes, spheres, cylinders, rulers, tape measures, or calibration grids can be used to align the 3D model by scaling, rotating, and translating it to fit the reference object. Blender, MeshLab, or CloudCompare are some examples of software that support reference objects. Lastly, coordinate systems such as WGS84 or UTM (global) or Cartesian or polar (local) can be used to align the 3D model by assigning it a coordinate system that matches the real-world coordinate system. QGIS, ArcGIS, or Geomagic are some examples of software that support coordinate systems.

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Arnauld Dumont

CEO at DimEye Corp.
Report contribution
There are many ways to align photogrammetry data and many strategies depending on the quality (accuracy and geometrical configuration), the quantity (redundancy), and the nature (theoretical or measured) of alignment data that is available. Most of the time, alignment will be based on best-fit routines (least square method), possibly weighted if all the alignment data doesn't have the same accuracy. It can be free (free-network adjustment) or prioritize some features (axis, points, geometrical features). The scale can be fixed or free, depending again on the survey's accuracy and the alignment data's accuracy. Finally, a gauge fit is sometimes preferred (minimizing the maximum deviation instead of the sum of squares).

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3 What are the challenges and limitations of aligning photogrammetry data?

Aligning photogrammetry data is not always straightforward or easy. Challenges and limitations, such as the availability and quality of the images, the complexity and variability of the object or scene, and the accuracy and precision of the control points, reference objects, and coordinate systems, can all affect the quality and accuracy of the alignment. Poorly captured images may make alignment difficult or impossible. Smooth, flat, reflective, or transparent objects can lead to inaccurate or unreliable alignment. Inaccurate, imprecise, or inconsistent control points, reference objects, or coordinate systems may cause erroneous or misleading alignment.

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4 How to evaluate and improve the alignment of photogrammetry data?

After aligning photogrammetry data, it is important to evaluate and improve the alignment to ensure its quality and accuracy. To do this, one should check the residuals, reprojection, and visual appearance. Residuals measure the error and uncertainty of the alignment, and should be as small as possible and evenly distributed. Reprojection should be as close as possible to the original images. Visual appearance should be as realistic and convincing as possible. If any of these are not up to par, the alignment may need to be adjusted, corrected, or enhanced.

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5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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How can you accurately align photogrammetry data in post-processing?

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1 Why align photogrammetry data?

2 How to align photogrammetry data in post-processing?

3 What are the challenges and limitations of aligning photogrammetry data?

4 How to evaluate and improve the alignment of photogrammetry data?

5 Here’s what else to consider

Computer Graphics

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How can you accurately align photogrammetry data in post-processing?

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2

3

4

5

1 Why align photogrammetry data?

2 How to align photogrammetry data in post-processing?

3 What are the challenges and limitations of aligning photogrammetry data?

4 How to evaluate and improve the alignment of photogrammetry data?

5 Here’s what else to consider

Computer Graphics

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