GENERATION OF A BI-DIRECTIONAL TEXTURE FUNCTION

Abstract

Described herein is a method for generating a bi-directional texture function (BTF) of an object, the method including 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, and adapting the initial BTF to the captured spectral reflectance data), thus, gaining an optimized BTF.

Also described herein are respective systems for generating a bi-directional texture function of an object.

Claims

1. A method for generating a bi-directional texture function (BTF) of an object, the method comprising at least the following steps: measuring an initial BTF (103) for the object using a camera-based measurement device, capturing spectral reflectance data (105) for the object for a pre-given number of different measurement geometries using a spectrophotometer, and adapting the initial BTF (103) to the captured spectral reflectance data (105), thus, gaining an optimized BTF (107).

2. The method according to claim 1, wherein the camera-based measurement device creates a plurality of images of the object at different viewing angles, at different illumination angles, for different illumination colors and/or for different exposure times, thus providing a plurality of measurement data considering a plurality of combinations of illumination angle, viewing angle, illumination color and/or exposure time.

3. The method according to claim 2, wherein the images with different illumination color and different exposure time, but with equal illumination angle and viewing angle are combined to images with high dynamic range, respectively.

4. The method according to claim 1, wherein adapting the initial BTF (103) to the captured spectral reflectance data (105), thus, gaining an optimized BTF (107), comprises segmenting the initial BTF (103) into different terms, each term comprising a set of parameters, and optimizing the parameters of each term separately using the captured spectral reflectance data (105).

5. The method according to claim 4, wherein the initial BTF (103) is segmented into two main terms, a first term being a homogeneous bi-directional 27843-1815 181690US01 reflectance distribution function (BRDF) which describes reflectance properties of the object depending only on the measurement geometry and the second term being a texture function which accounts for a spatially varying appearance of the object.

6. The method according to claim 5, wherein the first term is divided into a first sub-term corresponding to a color table and a second sub-term corresponding to an intensity function, and the parameters of the initial BTF (103) are optimized to minimize a color difference between the spectral reflectance data (105) and the initial BTF (103) by optimizing in a first optimization step the parameters of the color table while the parameters of the intensity function are kept constant, and by optimizing in a second optimization step the parameters of the intensity function while the parameters of the color table are kept constant.

7. The method according to claim 6 wherein for the optimization of the color table for each spectral measurement geometry first CIEL*a*b* values are computed from the spectral reflectance data (105) and second CIEL*a*b* values are computed from the initial BTF (103), and correction vectors in a* and b* coordinates are computed by subtracting the second CIEa*b* values from the first CIEa*b* values and the correction vectors are component-wise interpolated and extrapolated for the complete range of viewing and illumination angles stored in the color table, the interpolated correction vectors are applied to the initial BTF (103) CIEL*a*b* values for each spectral measurement geometry stored in the color table and the corrected BTF CIEL*a*b* values are transformed to linear sRGB coordinates which are normalized and finally stored in the color table.

8. The method according to claim 7 wherein a multilevel B-Spline interpolation algorithm is used for the component-wise interpolation and extrapolation of the correction vectors.

9. The method according to claim 6, wherein for optimization of the parameters of the intensity function a cost function is defined based on the sum of the color differences across all spectral reflectance measurements geometries.

10. The method according to claim 9 wherein the cost function is supplemented by a penalty function which is designed to take specific constraints into account.

11. The method according to claim 9 wherein the initial BTF (103) is evaluated at the different spectral reflectance measurement geometries and the resulting CIEL*a*b* values are compared to the CIEL*a*b* values from the spectral reflectance measurements using a weighted color difference formula and the parameters of the intensity function are optimized using a non-linear optimization method so that the cost function is minimized.

12. The method according to claim 6 wherein the first and the second optimization steps are run repeatedly/iteratively to further improve an accuracy of the optimized BTF (107).

13. A system for generating a bi-directional texture function (BTF) of an object, the system comprising: a camera-based measurement device which is configured to measure an initial BTF (103) for the object, a spectrophotometer which is configured to capture spectral reflectance data (105) for the object for a pre-given number of different measurement geometries, and a computing device which is in communicative connection with the camera-based measurement device and with the spectrophotometer, respectively, and which is configured to receive via the respective communicative connection the initial BTF (103) and the captured spectral reflectance (105) data for the object, and to adapt the initial BTF (103) to the captured reflectance data (105), thus gaining an optimized BTF (107).

14. The system according to claim 13 which is configured to perform a method comprising at least the following steps: measuring an initial BTF (103) for an object using a camera-based measurement device, capturing spectral reflectance data (105) for the object for a pre-given number of different measurement geometries using a spectrophotometer, and adapting the initial BTF (103) to the captured spectral reflectance data (105), thus, gaining an optimized BTF (107).

15. A computer system comprising: a computer unit; and a computer readable program with program code which causes, when the program is executed on the computer unit, to perform the following: acquiring and receiving an initial BTF (103) for an object and spectral reflectance data (105) for the object wherein the initial BTF (103) being measured by a camera-based measurement device, and the spectral reflectance data (105) are captured by a spectrophotometer for a pre-given number of different measurement geometries; and fitting the spectral reflectance data (105) with the initial BTF (103) by adapting parameters of the initial BTF (103) accordingly, thus obtaining an optimized BTF (107).

16. The method according to claim 9 wherein the cost function is supplemented by a penalty function which is designed to take specific constraints into account, such constraints comprising keeping the parameter values in a valid range.

17. The method according to claim 12 wherein the number of iterations is pre-defined.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0073] FIG. 1 shows a flowchart of a process that may be completed in accordance with an exemplary embodiment of the claimed method.

DETAILED DESCRIPTION

[0074] The present disclosure provides a method for determining a BTF of an object and associated systems. FIG. 1 provides a flowchart illustrating a process that may be executed in accordance with various embodiments of the claimed method and systems. Starting at step 102, an object is placed in a camera-based measurement device for measuring an initial BTF 103 of the object. The initial BTF 103 is gained as outcome of the measurement. At step 104 the object is placed in a spectrophotometer which is configured to capture respective spectral reflectance data for the object for a pre-given number of different measurement geometries. As a result, spectral reflectance data 105 (reflectance spectra for the different spectral measurement geometries) for the object are obtained for the limited number of different measurement geometries of the spectrophotometer. When reflectance spectra (reflectance data) have been captured at each desired (pre-given) measurement geometry, at step 106 the initial BTF 103 is adapted to the captured spectral reflectance data 105 by adapting the parameters of the initial BTF accordingly. As a result, an optimized BTF 107 is obtained.