## 1 Introduction  
Polarizers are core components of thin film transistor-liquid crystal display (TFT-LCD) panels widely used in display screens for products such as computers, mobile phones, digital cameras, and televisions. Typical polarizers are approximately 0.3 mm thick and composed of six transparent polymer films. During production and transportation, polarizers are prone to aesthetic defects such as impurities, scratches, stains, bubbles, dents, and residual glues. These defects may exist in any layer of a film, degrading the quality of the liquid crystal panels or even causing them to fail. Therefore, it is necessary to inspect each polarizer for aesthetic defects before attaching to screen panels. With the increasing application of machine vision and artificial intelligence in factory automation, numerous engineering-based practices for TFT-LCD industry and film defect detection have emerged [1]. [PERSON] et al. [2] reported a three-phase framework for embedding data mining and machine learning techniques for TFT-LCD manufacturing process. [PERSON] et al. [3] used a linear-array charge-coupled device camera to detect spot and line defects on polarizers. [PERSON] et al. [4] reported a neural network method to recognize six kinds of microdefects of color filters. [PERSON] et al. [5] proposed a cost-effective optical detection system to detect tiny bump defects on polarizers. Their system inspects an area of \(36\mathrm{mm}\times 27\mathrm{mm}\) within \(0.3\,\mathrm{s}\). [PERSON] et al. [6] proposed an unsupervised learning-based feature-level fusion approach to detect mura defects in TFT-LCD panels. In our previous work, a structured backlight was used to enhance the contrast of imaging, thus facilitating detection of polarizers' defects [7]. Despite all the effort, human visual inspection is nonetheless the primary method adopted in polarizer production. Besides the detection difficulty and the performance and efficiency requirement of the automated inspection, another important reason is that some detected microdefects are not real defects. These minor defects disappear or recover slowly after being attached to glass. Even human vision may fail to distinguish between real or not. It leads to a high false-positive rate. A subset of these defective samples is randomly sampled and sent for further inspection with some kind of microscopes, such as laser confocal microscopes. From the three-dimensional (3D) size information, we empirically tell if a microdefect is real or not. Obtaining the 3D contour of microdefects efficiently is thus a crucial task. To this end, researchers have come up with solutions. [PERSON] et al. [8] used ultrasonic-array total focusing technology to measure the depth of surface cracks (<1 mm). [PERSON] et al. [9] reported a stereoscopic fluorescence profilometry to measure the thickness and morphology of transparent films. More recently, [PERSON] et al. [10] proposed a new method of integrating robot arms and a confocal microscope to measure the surface roughness of objects. For high-precision applications, scanning electron microscopy can detect nanoscale defects [11]. [PERSON] et al. [12] used a circle-structure light to measure and inspect inner surface. However, the measurement range of these methods is extremely limited, and we also need to know the exact position of defects in advance. This is inefficient and unsuitable for industrial automated inspection. As far as we know, few studies have addressed the 3D estimation of polarizers' microdefects that take accuracy and efficiency into account simultaneously. In our previous studies [7; 13; 14; 15], we have focused on an imaging mechanism for transparent microdefects, such as dents and bumps, and proposed a so-called saturated imaging method to obtain high-contrast defect images. Through simulation and experimentation, we have found that the saturated image of a defect varies regularly with its stereo shape.