We propose an innovative method of material classification based on the imaging model of pulsed time-of-flight (ToF) camera integrated with the unique signature that describes physical properties of each material named reflection point spread function (RPSF). First, the optimization method reduces the effect of material surface interreflection, which would affect RPSF and lead to decreased accuracy in classification, by alternating direction method of multipliers (ADMM). A method named feature vector normalization is proposed to extract material RPSF features. Second, according to the nonlinearity of the feature vectors, the structure of hidden layer neurons of radial basis function (RBF) neural network is optimized based on singular value decomposition (SVD) to improve generalization. Finally, the similar appearance of plastics and metals are classified on turntable-based measurement system by own design. The average classification accuracy reaches 93.3%, and the highest classification accuracy reaches 94.6%.

我们提出了一种创新的材料分类方法，该方法基于脉冲飞行时间（ToF）相机的成像模型并结合了独特的签名，该签名描述了每种材料的物理特性，称为反射点扩散函数（RPSF）。首先，优化方法通过交替方向乘数法（ADMM）来减少材料表面相互反射的影响，该影响会影响RPSF，并导致分类精度降低。提出了一种称为特征向量归一化的方法来提取材料的RPSF特征。其次，根据特征向量的非线性，基于奇异值分解（SVD）对径向基函数（RBF）神经网络的隐层神经元的结构进行优化，以提高通用性。最后，塑料和金属的相似外观通过自己设计在基于转盘的测量系统上进行分类。平均分类精度达到93.3％，最高分类精度达到94.6％。