3D reconstruction is a fundamental task in robotics and AI, providing a prerequisite for many related applications. Fringe projection profilometry is an efficient and non-contact method for generating 3D point clouds out of 2D images. However, during the actual measurement, it is inevitable to experiment with translucent objects, such as skin, marble, and fruit. Indirect illumination from these objects has substantially compromised the precision of 3D reconstruction via the contamination of 2D images. This paper presents a fast and accurate approach to correct for indirect illumination. The essential idea is to design a highly suitable network architecture founded on a precise error model that facilitates accurate error rectification. Initially, our method transforms the error generated by indirect illumination into a sine series. Based on this error model, the multilayer perceptron is more effective in error correction than traditional methods and convolutional neural networks. Our network was trained solely on simulated data but was tested on authentic images. Three sets of experiments, including two sets of comparison experiments, indicate that the designed network can efficiently rectify the error induced by indirect illumination.

中文翻译：

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用于 3D 重建中间接照明的误差模型和简洁的时间网络


3D 重建是机器人技术和 AI 的一项基本任务，为许多相关应用提供了先决条件。条纹投影轮廓测量法是一种从 2D 图像生成 3D 点云的有效非接触式方法。但是，在实际测量过程中，不可避免地会尝试使用半透明物体，例如皮肤、大理石和水果。这些物体的间接照明会污染 2D 图像，从而严重损害 3D 重建的精度。本文提出了一种快速准确的间接照明校正方法。其基本思想是设计一个高度合适的网络架构，该架构建立在精确的错误模型之上，有助于准确的错误纠正。最初，我们的方法将间接照明产生的误差转换为正弦级数。基于该误差模型，多层感知器比传统方法和卷积神经网络更有效地进行纠错。我们的网络仅根据模拟数据进行训练，但在真实图像上进行了测试。3 组实验，包括 2 组对比实验，表明所设计的网络可以有效地纠正间接照射引起的误差。