Deep learning-enabled anti-ambient light approach for fringe projection profilometry

Abstract

Achieving high-quality surface profiles under strong ambient light is challenging in fringe projection profilometry (FPP) since ambient light inhibits functional illumination from exhibiting sinusoidal stripes with high quantization levels. Conventionally, large-step phase shifting approaches are presented to enhance the anti-interference capability of FPP, but the image acquisition process in these approaches is highly time-consuming. Inspired by the promising performance of deep learning in optical metrology, we propose a deep learning-enabled anti-ambient light (DLAL) approach that can help FPP extract phase distributions from a single fringe image exposed to unbalanced lighting. In this work, the interference imposed by ambient light on FPP is creatively modeled as ambient light-induced phase error (ALPE). Guided by the ALPE model, we generate the dataset by precisely adjusting the stripe contrast before performing active projection, overcoming the challenge of collecting a large sample of fringe images with various illumination conditions. Driven by the novel dataset, the generated deep learning model can effectively suppress outliers among surface profiles in the presence of strong ambient light, thereby implementing high-quality 3D surface imaging. Experimentally, we verify the effectiveness and adaptability of the proposed DLAL approach in both indoor and outdoor scenarios with strong irradiation.

3D reconstruction of single-image based on deep learning and fringe projection profilometry

“…Although the process from deformed fringe maps to depth can only rely on a single image as input, due to the high demand of datasets and network model, the final reconstruction accuracy of the related work cannot exceed that of traditional reconstruction methods. Other researchers have applied deep learning to transform fringe patterns into potentially intermediate quantities in the phase solution process [21][22][23][24][25][26][27][28] , employing convolutional neural networks in different ways to predict the numerator and denominator terms of the arctangent function of the wrapped phase. LI 23 et al proposed a series of composite stripe encoding strategies to achieve the extraction of the wrapped phase of an object directly from a single composite stripe map with absolute phase.…”