In the fields of computer graphics, computer vision, and medical imaging, scientists have spent many years working out how they can improve imaging and create models of objects taken from images. This has been a complicated process and they have faced many challenges. However, one area in which they have had some success is 3D reconstruction models, and these are something that you can create using several methods and there are a variety of applications for this technology. Here is an overview of what 3D reconstruction models are, how they are created, and their applications.
What Are 3D Reconstruction Models?
According to Wikipedia, 3D reconstruction models are models made using a process called 3D reconstruction. This is a process that involves capturing the appearance and shape of real objects
How Are 3D Reconstruction Models Created?
There are two main methods of creating 3D reconstruction models; active methods and passive methods. Active methods are also known as range data methods, and these use a numerical approximation approach to reconstruct the 3D profile and build the model. Using this method involves active interference with the reconstructed object, by using either radiometric or mechanical rangefinders. A mechanical method would use depth gauges to measure distances to an object that is rotating on a turntable. Examples of radiometric methods used include ultrasound, microwaves, and moving light sources. The difference between active methods and passive methods is that passive methods do not interfere with the object. Instead, they use sensors to measure the radiance from the surface of the object and then use this for image understanding. If passive methods are used, it is possible to apply 3D reconstruction to a greater number of applications.
Two passive methods are widely used; monocular cues methods and binocular stereo vision. In the former category, shape-from-shading is widely used, says Science Direct. Shade information is collected from an object to learn about the depth of normal information on the object, and this allows the reconstruction of the object. Another form of a monocular cues method is photometric stereo, and this is simply a more sophisticated version of shape-from-shading. The third monocular cues method that is widely used is shape-from-texture. This is used to find the depth of normal information on the surface of an object by using hints from the distortion and perspective of 2D images. When binocular stereo vision is used, multiple images are collected to obtain the 3-dimensional geometric information about an object. Two cameras are used simultaneously to collect the images from different angles, or one camera is used to take multiple photographs from different perspectives. An advantage of using binocular stereo vision methods over monocular cues methods is that this is a more direct approach.
3D Reconstruction Models and Virtual Reality Applications
One application for 3D reconstruction models is virtual reality. In a paper by Rong-Hua Liang, Zhi-Geng Pan, and Chun Chen published on Springer, 3D human face model reconstruction is a vital element of generating facial animations that are used in virtual reality. This poses some problems, such as creating a realistic human face and the collection of images with corresponding facial features. According to this journal, there is now a move towards using new algorithms based on binocular stereo vision methods is helping to create more realistic facial images for virtual reality.
3D Reconstruction Models and Uses in Medical Imaging
One of the most important applications for 3D reconstruction models is in the medical field. In relation to medical imaging, the creation of reconstruction models is referred to as Iterative reconstruction. Iterative algorithms are used for certain medical imaging techniques to reconstruct either 2D or 3D images. Iterative algorithms are mathematical and scientific sets of instructions that are created by the repetition of a process that generates a sequence of outcomes.
The Advantages of 3D Reconstruction Models for Medical Imaging
The main advantage of using 3D reconstruction models is that they provide better imaging than some of the traditional methods of collecting medical images. This particularly applies to medical imaging that can suffer from interference along ray paths or where noise statistics are poor, thus producing unclear images. A further advantage is that is can give images form different angles. This then provides the medical professionals with a larger amount of data and may reveal something that would otherwise have been missed form a different angle. Some forms of imaging are negatively affected by hardware limitations or by the fact that they take time. These are issues that the use of 3D reconstruction models can overcome as they are faster and more efficient than many other techniques. Finally, one type of imaging that has been significantly improved with the use of 3D reconstruction models is Magnetic Resonance Imaging (MRI). It can provide the extended modeling of physical processes and it allows medical professionals to use improved regularization techniques.
What About Models That Change Shape?
If a model changes shape over time, then the process of capturing images and creating models is called either non-rigid reconstruction of Spatio-temporal reconstruction. This takes reconstruction models one step further as these are 4D reconstructions. This is the process of capturing the shape and appearance of real objects that will gradually change over time. The 4D reconstruction models reflect this behavior as they will also change over time. This technology is generally used in computer vision and computer graphics.
3D Reconstruction Models – The Final Verdict
Although 3D reconstruction is a rather complicated process that has several challenges, it is a technology that has many applications. Currently, it is predominantly used to create realistic imaging for virtual reality applications and for medical imaging. This technology is something that scientists are still trying to improve, but there have been big advances in this technology that have made it a better option than many other techniques used in computer graphics, computer vision, and medical imaging.