Bi-directional Texture Function Generation for Car Paint Color Accuracy
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Solution Overview
Problem
Current car paint color design processes using physical samples are costly and time-consuming, and the color information obtained from camera-based measurement devices is not sufficiently accurate for design reviews, especially when inferring how a coating would appear on different shapes or in varying light conditions.
Innovation Solution
A method that involves measuring an initial bi-directional texture function (BTF) using a camera-based device and then adapting it with additional spectral reflectance data from a spectrophotometer to enhance color accuracy, capturing the sparkling and spatially varying appearance of car paints, by segmenting the BTF into BRDF and texture terms and optimizing parameters using multilevel B-Spline interpolation and non-linear optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a camera-based measurement device is used to measure BTF, then the measurement speed and ability to capture spatially varying appearance are improved, but the color accuracy and reliability are insufficient
Solution Approach 1:
The patent combines two different measurement devices: a camera-based measurement device for capturing spatially varying appearance and a spectrophotometer for measuring accurate color information. The BTF from the camera device and spectral reflectance data from the spectrophotometer are merged through adaptation to create an optimized BTF that achieves both high measurement speed and high color accuracy.
Solution Approach 2:
The patent introduces an intermediary adaptation process that bridges the two measurement systems. The initial BTF from the camera-based device is adapted using spectral reflectance data from the spectrophotometer as a reference, allowing the transfer of accurate color information to the faster camera-based measurement system.
2Reliability
If only physical samples are used for color design, then the color information is obtained directly, but the process is costly and time-consuming
Solution Approach 1:
The patent performs preliminary action by creating a digital BTF model that can be used to simulate the appearance of car paint on arbitrary shapes and in arbitrary light conditions before physical production. This allows virtual assessment of color characteristics, reducing the need for multiple physical sample iterations.
Solution Approach 2:
The patent creates a digital copy (BTF) of the physical paint sample's appearance properties. This digital model captures the spatially varying appearance and color characteristics, allowing virtual evaluation without repeatedly painting and measuring physical samples.
3Productivity
If small flat panels are painted for testing, then the painting cost and time are reduced, but it is difficult to infer how the coating would look on different shapes
Solution Approach 1:
The patent creates a universal BTF model that can be applied to represent the appearance of car paint on any arbitrary shape and under any arbitrary light conditions. This single digital model serves multiple evaluation purposes, replacing the need for multiple physical samples on different test panels.
Data Source
AI summary
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, andadapting 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.