Stereo photometry creates 3D models from 2D images based on how objects reflect or refract light. Multiple source images are needed, and shadows and highlights help calculate surface normals for constructing a 3D model. Glossy surfaces are easiest to measure, and single light sources work best. It can be used for real-time computer vision processing and creating accurate 3D models for historical or analytical purposes.
In computer graphics and, more specifically, computer vision applications, stereo photometry is the process of creating a three-dimensional (3D) model or representation of a two-dimensional (2D) image based on how the objects in the image reflect or refract lightly. When a stereo photometric algorithm is applied to an object, more than one source image must be available for analysis. For each of the source images to be used, the object should generally appear to be in a static position, while the light source is moved to reveal different aspects of the object’s surface. Simple stereo photometric imaging methods need to know the position of lights in relation to the object and work best when the object is made of a single material so that lights and shadows can be measured in predictable ways. Much more advanced algorithms and techniques don’t require as much information up front and can make several guesses, or even interpolate surfaces, to complete a partially obstructed image.
The basic concept of photometric stereo involves taking several images of an object with the light source in each image moved around the object while the object remains in the same position. By measuring exactly how shadows and highlights fall on the surface of the object in each image, it is possible to calculate surface normals, which is the direction a surface faces. After compiling information about the normals of an object’s measurable areas across a series of 2D images, a 3D model of the object can be constructed.
Factors such as depth of shadows and intensity of lights help determine the different heights of surface topography. Objects that have a glossy surface are easiest to measure with stereo photometrics, while objects made from a material with more subtle shadows, such as a soft fabric, can be more difficult. An object that has a highly reflective surface, such as polished chrome, may have some problems without proper image adjustment, because the reflections may give false results. The algorithms work best when there is only a single light source and no shadows cast, as opposed to an area of light, multiple light sources, or light shining through a window or other opening.
There are several uses for stereo photometric techniques. In real-time computer vision processing, it can be used to determine the depth of objects within a scene. It can also be used to create accurate 3D models of objects in photographs for historical, archival or analytical purposes.
Protect your devices with Threat Protection by NordVPN
