In this series of short articles, we will try to explain very basics of engineering workflow that include point clouds, at any point. We are aware, that most people have some knowledge of the topic, but the ultimate goal of this series is to inform our current or potential partners, how we create our products and how to use them effectively without huge experience.
WHAT IS A POINT CLOUD?
Technically point cloud is a database containing points in the three-dimensional coordinate system. However, from the typical workflow perspective, the only important thing is, that point cloud is a very accurate digital record of an object or space. It is saved in form of a very large number of points that cover surfaces of a sensed object.
Points in a point clouds are always located on the external surfaces of visible objects, because this are the spots, where ray of light from the scanner reflected from an objects.
If individual points size is big enough in the certain view or zoom setting the point cloud could be perceived as the continual surface. If the distance between points is slightly larger, then it’s size, then we can clearly see, that this image is made of individual points, but still, our brain can relatively easily pick up shapes of an object from such image.
It is essential to understand that the point cloud is a set of individual, unrelated points with defined position and colour. This makes point clouds quite easy to edit, display and filter.
Using individual, unrelated points is a key to point clouds usefulness, because points are objects that are easiest to handle a large amount of. A computer does not have to care about scale, rotation and relations to other objects. Only position and colour are things that matter for computation. This makes point clouds quite easy to edit, display and to filter data.
The only real downside in the point cloud interpretation process is that in most cases requires human involvement. Some pieces of software are certainly capable of finding certain patterns and features but still, they cannot easily categorize, and covert contents of a point cloud.
ACQUISITION OF POINT CLOUDS
The key factor in acquiring point cloud data is the access/visibility to scanned surfaces. It is important to remember, that point cloud is obtained by visible access to real objects. Regardless of the method of acquisition (scanner or photos). It is impossible to obtain points on the surfaces that are not visible from the position from which we collect data. This means that to cover all objects that have to combine many scanning positions.
We are using term density to describe resolution on the collected dataset this usually means the distance from a point to point. Less dense point clouds are obviously much quicker to capture.
Most point cloud databases contain not only position of a point, but also a description of visual properties, as a colour of an object or it’s reflectivity. All of such may or may not be included within point cloud and they are yet another factor that affects the time the time of acquiring scan.
You can read more about point cloud colour on our website.