Use of a bi-directional texture function, BTF, of an object in the design and development of automobiles, wherein the BTF has been generated by a computer-implemented method comprising at least the following steps: - measuring an initial BTF for the object using a camera-based measurement device, - capturing spectral reflectance data for the object for a pre-given number of different measurement geometries using a spectrophotometer; - adapting the initial BTF of formula by segmenting f x ι ̄ o ̄ = χ ι ̄ o ̄ a π + ∑ k = 1 3 f S k, a k, F 0, k CT ι ̄ o ̄ + Ξ x ι ̄ o ̄ with x: surface coordinates of the sample/object i, o: illumination and observation/viewing directions at the basecoat of the sample χ ( i, o): color table depending on illumination and observation direction a: Albedo or diffuse reflectivity f S k, a k, F 0, k CT ι ̄ o ̄: the k-th Cook-Torrance lobe, corresponding to a Bi-directional Reflectance Distribution Function (BRDF) that describes the glossiness of a microfacet surface S k: weight for the k-th Cook-Torrance lobe α k: parameter for the Beckmann distribution of the k-th Cook-Torrance lobe F 0,k: Fresnel reflectivity for the k-th Cook-Torrance lobe Ξ( x, i, o): table of spatial texture images depending on illumination and observation direction into a term χ ι ̄ o ̄ a π + ∑ k = 1 3 f S k, a k, F 0, k CT ι ̄ o ̄ and a term Ξ( x, i, o), further dividing the term into a first sub-term corresponding to a color table depending on illumination and observation direction χ ( i, o) and a second sub-term corresponding to an intensity function a π + ∑ k = 1 3 f S k, a k, F 0, k CT ι ̄ o ̄, and minimizing a color difference between the captured spectral reflectance data and the initial BTF by optimizing in a first optimization step the parameters of the first sub-term while the parameters of the second sub-term are kept constant, and by optimizing in a second optimization step the parameters of the second sub-term while the parameters of the first sub-term are kept constant, thus gaining an optimized BTF, characterized in that the BTF is used to design a vehicle part which matches the optical appearance of the part(s) adjacent to it in a finished vehicle. - measuring an initial BTF for the object using a camera-based measurement device, - capturing spectral reflectance data for the object for a pre-given number of different measurement geometries using a spectrophotometer; - adapting the initial BTF of formula by segmenting f x ι ̄ o ̄ = χ ι ̄ o ̄ a π + ∑ k = 1 3 f S k, a k, F 0, k CT ι ̄ o ̄ + Ξ x ι ̄ o ̄ with x: surface coordinates of the sample/object i, o: illumination and observation/viewing directions at the basecoat of the sample χ ( i, o): color table depending on illumination and observation direction a: Albedo or diffuse reflectivity f S k, a k, F 0, k CT ι ̄ o ̄: the k-th Cook-Torrance lobe, corresponding to a Bi-directional Reflectance Distribution Function (BRDF) that describes the glossiness of a microfacet surface S k: weight for the k-th Cook-Torrance lobe α k: parameter for the Beckmann distribution of the k-th Cook-Torrance lobe F 0,k: Fresnel reflectivity for the k-th Cook-Torrance lobe Ξ( x, i, o): table of spatial texture images depending on illumination and observation direction into a term χ ι ̄ o ̄ a π + ∑ k = 1 3 f S k, a k, F 0, k CT ι ̄ o ̄ and a term Ξ( x, i, o), further dividing the term into a first sub-term corresponding to a color table depending on illumination and observation direction χ ( i, o) and a second sub-term corresponding to an intensity function a π + ∑ k = 1 3 f S k, a k, F 0, k CT ι ̄ o ̄, and minimizing a color difference between the captured spectral reflectance data and the initial BTF by optimizing in a first optimization step the parameters of the first sub-term while the parameters of the second sub-term are kept constant, and by optimizing in a second optimization step the parameters of the second sub-term while the parameters of the first sub-term are kept constant, thus gaining an optimized BTF, x: surface coordinates of the sample/object i, o: illumination and observation/viewing directions at the basecoat of the sample χ ( i, o): color table depending on illumination and observation direction a: Albedo or diffuse reflectivity f S k, a k, F 0, k CT ι ̄ o ̄: the k-th Cook-Torrance lobe, corresponding to a Bi-directional Reflectance Distribution Function (BRDF) that describes the glossiness of a microfacet surface S k: weight for the k-th Cook-Torrance lobe α k: parameter for the Beckmann distribution of the k-th Cook-Torrance lobe F 0,k: Fresnel reflectivity for the k-th Cook-Torrance lobe Ξ( x, i, o): table of spatial texture images depending on illumination and observation direction into a term χ ι ̄ o ̄ a π + ∑ k = 1 3 f S k, a k, F 0, k CT ι ̄ o ̄ and a term Ξ( x, i, o), further dividing the term into a first sub-term corresponding to a color table depending on illumination and observation direction χ ( i, o) and a second sub-term corresponding to an intensity function a π + ∑ k = 1 3 f S k, a k, F 0, k CT ι ̄ o ̄, and minimizing a color difference between the captured spectral reflectance data and the initial BTF by optimizing in a first optimization step the parameters of the first sub-term while the parameters of the second sub-term are kept constant, and by optimizing in a second optimization step the parameters of the second sub-term while the parameters of the first sub-term are kept constant, thus gaining an optimized BTF,