The application of 3D capturors is the process of using digital cameras and pre-designed light to capture the information of shape and appearance of real objects. This process provides a simple way of acquiring 3D models of unparalleled details of objects and realizes 3D image modeling by scanning them from the real world.
The purpose of a 3D camera is usually to create a point cloud of points on the surface of the subject. These points can then be used to extrapolate the shape of the object (a process called reconstructino). 3D cameras are very analogous to cameras. Like cameras, they have a cone-like field of view, and like cameras, they can only collect information about surfaces that are not obscured. While a camera collects color information about surfaces within its field of view, 3D cameras collect distance information about surfaces within its field of view. The “picture” produced by a 3D camera describes the distance to a surface at each point in the picture.
For most situations, a single scan will not produce a complete 3D image model of the object. Multiple scans from many different directions are usually required to obtain information about all sides of the objects. These scans are merged to create a complete 3D image model.

There are two types of 3D cameras, which are contact and non-contact. Non-contact 3D cameras can be further divided into two main categories, active cameras and passive cameras. There are a variety of technologies that fall under each of these categories.
Active 3D cameras emit some kind of radiation or light and detect its reflection in order to probe an object or environment. Possible types of radiation used include light, ultrasound or x-ray.
Time of Flight Techinque:
The time-of-flight 3D laser camera is an active 3D camera that uses laser light to probe the object. At the heart of this type of 3D camera is a time-of-flight laser range finder. The laser range finder finds the distance of a surface by timing the round-trip time of a pulse of light. A laser is used to emit a pulse of light and the amount of time before the reflected light is seen by a detector is timed. Since the speed of light is a known, the round-trip time determines the travel distance of the light, which is twice the distance between the 3D camera and the object surface. The laser range finder only detects the distance of one point in its direction of view. Thus, the 3D capturor scans its entire field of view one point at a time by changing the range finder’s direction of view to scan different points. The view direction of the laser range finder can be changed by either rotating the range finder itself, or by using a system of rotating mirrors. The latter method is commonly used because mirrors are much lighter and can thus be rotated much faster. Typical time-of-flight 3D laser capturors can measure the distance of 10,000 points every second.

The triangulation 3D laser capturor is also an active 3D laser capturor that uses laser light to probe the environment. This type of 3D laser capturor is identical to the time-of-flight 3D laser scanner except for the way in which the laser range finder determines distance. The triangulation laser range finder used in this 3D capturor shines a laser on the subject and a camera looks at the location of the laser dot. The laser and the camera are placed so that the direction of the laser and the view direction of the camera are not parallel. Depending on how far away the laser strikes a surface, the laser dot appears at different places in the camera’s field of view. This technique is called triangulation because the laser dot, the camera and the laser emitter form a triangle. The length of one side of the triangle, the distance between the camera and the laser emitter is known. The angle of the laser emitter corner is also known. The angle of the camera corner can be determined by looking at the location of the laser dot in the camera’s field of view. These three pieces of information fully determine the shape and size of the triangle and gives the location of the laser dot corner of the triangle.

Structured light 3D capturors project a pattern of light on the subject and look at the deformation of the pattern on the subject. The pattern maybe be one dimensional or two dimensional. An example of a one dimensional pattern is a line. The line is projected onto the subject using either an LCD projector or a sweeping laser. A camera, offset slightly from the pattern projector, looks at the shape of the line and uses a technique similar to triangulation to calculate the distance of every point on the line. In the case of a single-line pattern, the line is swept across the field of view to gather distance information one strip at a time. An example of a two dimensional pattern is a grid or a line strip pattern. A camera is used to look at the deformation of the pattern and a fairly complex algorithm is used to calculate the distance at each point in the pattern. A variety of other patterns can be used, each with their own advantages and disadvantages. The advantage of structured light 3D capturors is speed. Instead of scanning one point at a time, structured light capturors scan multiple points or the entire field of view at once. This reduces or eliminates the problem of distortion from motion. Some existing systems are capable of scanning moving objects in real-time.

Passive 3D capturors do not emit any kind of radiation and lights themselves, but instead rely on detecting reflected ambient radiation. Most 3D capturors of this type detect visible light because it is a readily available ambient radiation. Other types of radiation, such as infrared could also be used.

Stereoscopic 3D scanners usually employ two video cameras or mirrors, slightly apart, looking at the same scene. By analyzing the slight differences between the images seen by each camera/mirror, it is possible to determine the distance at each point in the images. This method is based on human stereoscopic vision.

The point clouds produced by 3D scanners are usually not used directly. Most applications do not use point clouds, but instead use polygonal 3D image models. The process of converting a point cloud into a polygonal 3D model is called reconstruction. Reconstruction involves finding and connecting adjacent points in order to create a continuous surface. Many algorithms are available for this purpose.