What Factors Affect Your 3D Scanning Details?

When evaluating a 3D scanner, camera resolution is often one of the first specifications people notice. A higher megapixel count indicates a greater number of image pixels, providing more information for data acquisition. However, camera resolution alone does not determine the level of detail captured in a 3D scan.

The field of view (FOV), point distance, optical performance, and data processing algorithms all contribute to the final level of detail. This guide examines how these parameters interact and how they influence a scanner's ability to capture fine geometric details.

For a visual introduction to these concepts, watch this episode of Metrology TechTalk.

Camera resolution refers to the number of pixels a camera uses to capture an image. Generally, a higher-resolution camera provides more pixels, allowing more image information to be recorded.

However, the total number of pixels is only part of the equation.

Consider a simple example: if you want to see a small object clearly, you have two options:

• Increase the number of pixels available to capture it.
• Make the object occupy a larger portion of the image.

For a camera with a fixed resolution, reducing the FOV generally increases the spatial sampling density, assuming comparable camera and imaging conditions.

This means that the same camera resolution can produce different spatial sampling densities depending on the scanning range.

Point Distance: A Key Parameter for Understanding 3D Detail

One important parameter directly related to spatial sampling is point distance. It refers to the spacing between neighboring points in a 3D point cloud. It indicates how densely the object's surface is represented by the captured 3D data.

Generally:

• Smaller point distance: Denser point distribution and a greater potential to capture fine geometric features.

• Larger point distance: Sparser point distribution, which may make smaller surface features harder to represent.

Under comparable scanning conditions, a smaller point distance generally provides a better basis for capturing fine geometric details.

However, point distance should not be confused with accuracy or resolution in the broader sense. It describes the spacing of sampled points, not necessarily how accurately each point represents the object's true position.

To understand this relationship more clearly, let's look at a real example.

The OptimScan Q12 features two pairs of 12.3-megapixel cameras designed for different scanning ranges. Although both camera pairs have the same resolution, their fields of view are different.

For a simplified theoretical estimation, the horizontal spatial sampling interval can be calculated by dividing the horizontal FOV by the number of horizontal pixels. A 12.3-megapixel camera has approximately 4,000 pixels across its horizontal direction.

• Small-range mode: 160 mm ÷ 4,000 pixels = 0.04 mm/pixel

• Large-range mode: 430 mm ÷ 4,000 pixels ≈ 0.11 mm/pixel
  

Note: The values above are simplified theoretical estimates based on horizontal FOV and approximate horizontal pixel count. They represent the spatial sampling interval at the image level and should not be interpreted as the scanner's specified point distance or measurement accuracy.

The difference is clear. Even though both camera pairs have the same resolution, the small-range mode distributes the available pixels across a much smaller area. This results in denser spatial sampling and a smaller theoretical sampling interval.

The difference becomes more apparent when comparing real scan data.

In the large-range mode, certain fine features may not be sufficiently represented. When switching to the small-range mode, these details can be captured more clearly thanks to the denser spatial sampling. This demonstrates why selecting an appropriate scanning range is important when working with objects that contain fine geometric features.

For applications that demand even finer details, OptimScan Q12 HD takes this principle further by offering a smaller FOV in its small-range scanning mode.

As demonstrated in the scan data comparison, text and other fine surface features become more clearly distinguishable. This makes the Q12 HD particularly relevant for applications where capturing intricate geometric details is a priority.

Other Factors Affecting 3D Scanning Detail 

Spatial sampling density and point distance are important considerations, but the final quality of 3D scan data is also influenced by the overall performance of the scanning system.

• Optical Performance

The optical system affects image quality and the scanner's ability to distinguish fine surface features. Lens performance, image clarity, and the quality of the acquired optical information all contribute to the resulting 3D data.

• Data Processing Algorithms

The reconstruction process converts captured image information into 3D measurement data. Data fitting, reconstruction, and optimization algorithms influence how effectively the acquired information is translated into the final point cloud.

These factors are particularly relevant to handheld laser scanners, which typically capture raw images at high frame rates. Their final 3D output is generated through multi-frame data processing, fitting, and optimization rather than being determined by an individual frame alone.

Consequently, the quality of the final 3D data depends on the combined performance of image acquisition, optical components, and software algorithms.

Conclusion: Key Parameters to Consider When Evaluating a 3D Scanner

A comprehensive evaluation of 3D scanning detail requires consideration of several interconnected specifications rather than camera resolution alone.

The appropriate combination of these parameters depends on the application, the dimensions of the scanned object, and the level of geometric detail required.