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Namely, given a surface X in Euclidean space R 3, the Gauss map is a map N: X → S 2 (where S 2 is the unit sphere) such that for each p in X, the function value N(p) is a unit vector orthogonal to X at p. The Gauss map is named after Carl F. Gauss. The Gauss map can be defined (globally) if and only if the surface is orientable, in which case ...
A texture map (left). The corresponding normal map in tangent space (center). The normal map applied to a sphere in object space (right). Normal map reuse is made possible by encoding maps in tangent space. The tangent space is a vector space, which is tangent to the model's surface. The coordinate system varies smoothly (based on the ...
A normal vector of length one is called a unit normal vector. A curvature vector is a normal vector whose length is the curvature of the object. Multiplying a normal vector by −1 results in the opposite vector, which may be used for indicating sides (e.g., interior or exterior).
The normalized vector û of a non-zero vector u is the unit vector in the direction of u, i.e., ^ = ‖ ‖ where ‖u‖ is the norm (or length) of u. [1] [2] The term normalized vector is sometimes used as a synonym for unit vector. A unit vector is often used to represent directions, such as normal directions.
Then there is a unique geodesic γ v:[0,1] → M satisfying γ v (0) = p with initial tangent vector γ′ v (0) = v. The corresponding exponential map is defined by exp p (v) = γ v (1). In general, the exponential map is only locally defined, that is, it only takes a small neighborhood of the origin at T p M, to a neighborhood of p in the ...
Its normalized form, the unit normal vector, is the second Frenet vector e 2 (t) and is defined as = ¯ ‖ ¯ ‖. The tangent and the normal vector at point t define the osculating plane at point t.
A reflection about a line or plane that does not go through the origin is not a linear transformation — it is an affine transformation — as a 4×4 affine transformation matrix, it can be expressed as follows (assuming the normal is a unit vector): [′ ′ ′] = [] [] where = for some point on the plane, or equivalently, + + + =.
In Cartesian space, the norm of a vector is the square root of the vector dotted with itself. That is, ‖ ‖ = Many important results in linear algebra deal with collections of two or more orthogonal vectors. But often, it is easier to deal with vectors of unit length. That is, it often simplifies things to only consider vectors whose norm ...