Color projector light intensity adaptive high dynamic range 3D measurement method
doi: 10.37188/CO.EN-2024-0038
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摘要:
单一曝光时间或单一投影强度的条纹投影轮廓术(FPP)系统方法受限于相机的动态范围，会导致图像的过饱和和欠饱和，从而造成点云缺失或精度降低。为了解决这一问题，有别于投影仪像素调制方法，我们利用彩色投影仪三通道LED投影强度可单独控制的特点，提出了投影仪三通道光强分离的方法，结合彩色相机，实现了单曝光、多光强图像采集。进一步地，将串扰系数应用到被测物体三通道反射率预测中，结合聚类与通道映射，建立了投影仪三通道电流与相机三通道图像光强的像素级映射模型，实现了最佳投影电流预测和高动态范围图像获取。我们所提出的方法只需一次曝光就能实现高动态范围场景的高精度三维数据获取，该方法的有效性已通过标准平面和标准台阶的实验进行了验证，相比于现有单曝光高动态方法显著降低了平均绝对误差(44.6%), 相比于多曝光融合方法所需要的采集图像数量显著减小(文中场景下图片数量减小70.8%)，提出的方法在各种 FPP 相关领域具有巨大潜力。
Abstract:The Fringe Projection Profilometry (FPP) system with a single exposure time or a single projection intensity is limited by the dynamic range of the camera, which can lead to overexposure and underexposure of the image, resulting in point cloud loss or reduced accuracy. To address this issue, unlike the pixel modulation method of projectors, we utilize the characteristics of color projectors where the intensity of the three-channel LED can be controlled independently. We propose a method for separating the projector's three-channel light intensity, combined with a color camera, to achieve single exposure and multi-intensity image acquisition. Further, the crosstalk coefficient is applied to predict the three-channel reflectance of the measured object. By integrating clustering and channel mapping, we establish a pixel-level mapping model between the projector's three-channel current and the camera's three-channel image intensity, which realizes the optimal projection current prediction and the high dynamic range (HDR) image acquisition. The proposed method allows for high-precision three-dimensional (3D) data acquisition of HDR scenes with a single exposure. The effectiveness of this method has been validated through experiments with standard planes and standard steps, showing a significant reduction in mean absolute error (44.6%) compared to existing single-exposure HDR methods. Additionally, the number of images required for acquisition is significantly reduced (by 70.8%) compared to multi-exposure fusion methods. This proposed method has great potential in various FPP-related fields.
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Figure 1. Hardware and algorithm flowchart. (a) System Schematic. (b) HDR Image Prediction. (c) Color multi-channel projector current adaptive modulation. (d) Crosstalk calibration process. (e) Color image channel separation and fusion.
Figure 2. Crosstalk calibration curve calculation. (a) Three-color image acquisition and channel separation. (b) Blue channel crosstalk fitting to green and red channels. (c) Green channel crosstalk fitting to blue and red channels. (d) Red channel crosstalk fitting to green and blue channels.
Figure 3. HDR image calculation. (a) Pre-acquisition image. (b) Reflectance calculation of the measured scene,b-1 is the reflectance coefficient of the
${I^{120}}(x,y;t)$ non-over-exposed region, b-2 is the distribution and fitting result of$ LB(x,y) $ and${I^{120}}(x,y;t)$ , b-3 is the reflectance coefficient of the${I^{120}}(x,y;t)$ over-exposed region, and b-4 the reflectance coefficient of the measured scene. (c) High dynamic range measured scene data.
Figure 4. Three-channel image crosstalk removal and three-channel HDR image acquisition.
Figure 8. HDR scene measurement experiment. (a) 3D data obtained using traditional methods. (b) 3D data obtained using the method proposed in this paper.
Figure 10. Highly reflective standards and step measurement result. (a) 3D data obtained by the traditional method. (b) 3D data obtained by Liu's method[26]. (c) 3D data obtained by the method in this paper. (d) planar RMSEs of the three methods.
Table 1. Crosstalk calibration results
$ {a_{ \to R}} $ $ {{\text{b}}_{ \to R}} $ $ {a_{ \to G}} $ $ {{\text{b}}_{ \to G}} $ $ {a_{ \to B}} $ $ {{\text{b}}_{ \to B}} $ R 1 0 0.209 3.081 0.129 4.789 G 0.268 3.307 1 0 0.193 5.537 B 0.082 3.995 0.314 3.194 1 0 
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Table 2. Comparison of measurement accuracy
Step distance (mm) Liu[26] proposed method values errors values errors 12.053 12.094 0.041 12.070 0.017 10.006 10.040 0.034 10.029 0.023 8.018 7.991 0.027 8.002 0.016 6.016 5.999 0.017 6.026 0.010
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