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Anything following a hash character (#) is a comment. # this is a comment An OBJ file may contain vertex data, free-form curve/surface attributes, elements, free-form curve/surface body statements, connectivity between free-form surfaces, grouping and display/render attribute information.
Cube maps provide a fairly straightforward and efficient solution to rendering stable specular highlights. Multiple specular highlights can be encoded into a cube map texture, which can then be accessed by interpolating across the surface's reflection vector to supply coordinates. Relative to computing lighting at individual vertices, this ...
The Phong reflection model was developed by Bui Tuong Phong at the University of Utah, who published it in his 1975 Ph.D. dissertation. [1] [2] It was published in conjunction with a method for interpolating the calculation for each individual pixel that is rasterized from a polygonal surface model; the interpolation technique is known as Phong shading, even when it is used with a reflection ...
The Blinn–Phong reflection model, also called the modified Phong reflection model, is a modification developed by Jim Blinn to the Phong reflection model. [1]Blinn–Phong is a shading model used in OpenGL and Direct3D's fixed-function pipeline (before Direct3D 10 and OpenGL 3.1), and is carried out on each vertex as it passes down the graphics pipeline; pixel values between vertices are ...
Specular highlights on a pair of spheres. A specular highlight is the bright spot of light that appears on shiny objects when illuminated (for example, see image on right). ). Specular highlights are important in 3D computer graphics, as they provide a strong visual cue for the shape of an object and its location with respect to light sources in the
Diffusion maps exploit the relationship between heat diffusion and random walk Markov chain.The basic observation is that if we take a random walk on the data, walking to a nearby data-point is more likely than walking to another that is far away.
FaceNet is a facial recognition system developed by Florian Schroff, Dmitry Kalenichenko and James Philbina, a group of researchers affiliated with Google.The system was first presented at the 2015 IEEE Conference on Computer Vision and Pattern Recognition. [1]
Photometric stereo analyzes multiple images of an object under different lighting conditions to estimate a normal direction at each pixel. Photometric stereo is a technique in computer vision for estimating the surface normals of objects by observing that object under different lighting conditions ().