Due to capturing all dimensions of the scan object the scanner needs all angles scanned for be results.

To stitch geometry from different angles together into one seamless model, all 3D capturing uses feature detection (calibration, which are the dots located on the turn-table to know what the scanner is capturing) and alignment to understand which parts need to go where via point clouds and cloud alignment.

Cloud alignment is aligning sets of 3D data point to reconstruct real-world geometry from iterative 3D. Commonly, each data point contains a position and sometimes a color, and the sets of 3D data points are referred to as a “point cloud”.

For the purpose of three-dimensional scanning, a point cloud is a set of x, y, z coordinate points in three- dimensional space that represents the external surface of an object. Our 3D scanner uses a depth sensor to capture data as point clouds by emitting an array of infrared beams which intersect and reflect off encountered surfaces at varying points; these points are then assigned the appropriate width, height, and depth coordinates at the point of contact.

Given the numerous infrared beams emitted by the depth sensor, a cohesive topological representation of the space can be represented by these points, forming a point cloud. A surface represented by a point cloud can be colored by assigning each point an RGB value captured by the color camera; each point is represented by a six-dimensional vector < x, y, z, r, g, b >.

For example, a digital point cloud representation would look like this:

Point Cloud in Scan Dimensions