提出了一种基于共位图像的逆向反射模型,可用于对非漫反射表面的非线性反射行为进行精确建模。该模型可以从像素值精确映射到法向量与入射光线方向的内积。实验仅需一张共位图像和一张多光谱条件下拍摄的RGB图像就可以实现高精度的光度立体视觉性能,大大缩短了拍照所需的时间。对于大批量生产的工件的表面检测而言,由于共位图像可以提前采集后供后续工件重复使用,故该技术可以以微秒级的拍摄速率来实现移动表面的在线检测。另一方面,由于该方法中使用了神经网络来训练近场光度立体视觉模型中的映射关系,省去了传统近场光度立体视觉中的迭代步骤同时提高了对于阴影点、高亮点等异常值的鲁棒性。经仿真和实验验证,该算法能够很好地在极少量图片条件下恢复非漫反射表面的法向量。

In this study, we propose an inverse reflectance model based on co-located images to precisely model the nonlinear reflection behavior of the non-diffuse reflective surfaces. The proposed model can accurately map the pixel value to the product of the normal vector and the light direction. We need to capture only one co-located image and one RGB image under multispectral conditions to ensure that photometric stereo vision can achieve a high-precision performance, so the time required to capture images is considerably reduced. To perform surface inspection in case of mass production, the proposed method can realize online detection of the moving surfaces at a microsecond shooting rate because the co-located image can be acquired in advance and used for the subsequent workpiece. However, the iterative steps applied in the traditional methods are omitted, and the robustness with respect to outliers, such as shadow points and highlights, is improved, because a neural network is used in the proposed method to train the near-field photometric stereo model. Furthermore, the results of simulation and experiment show that the algorithm can recover the normal vector of the non-diffuse surface well under the condition of very few images.