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The forensic science community raised the need for improved evidence recognition, collection, and visualization analytical instrumentation for field and laboratory use. While the 3D optical techniques for imaging static objects have been extensively studied, there is still a major gap between current knowledge and collecting high‐quality footwear and tire impression evidence. Among optical means for 3D imaging, digital fringe projection (DFP) techniques reconstruct 3D shape from phase information, achieving camera‐pixel spatial resolution. This paper presents a high‐resolution 3D imaging technology using DFP techniques dedicated to footwear and tire impression capture. We developed fully automated software algorithms and a graphical user interface (GUI) that allow anyone without training to operate for high‐quality 3D data capture. We performed accuracy evaluations and comparisons comparing with the commercial high‐end 3D scanner and carried out qualitative tests for various impressions comparing with the current practices. Overall, our technology achieves similar levels of accuracy and resolution with a high‐end commercially available 3D scanner, while having the merits of being (1) more affordable; (2) much easier to operate; and (3) more robust. Compared with the current practice of casting, our technology demonstrates its superiority because it (1) is non‐destructive; (2) collects more evidence detail than casts, especially when an impression is fragile; (3) requires less time and money to collect each piece of evidence; and (4) results in a digital file that can easily be shared with other examiners. [ABSTRACT FROM AUTHOR]

In structured light 3D imaging, achieving robust and accurate 3D reconstruction with a limited number of fringe patterns remains a challenge. In this study, we introduce SFNet, a symmetric fusion network that designed for high-speed, high-quality 3D surface measurement using just two fringe images. The SFNet employs separate encoders and decoders for each fringe input to estimate its phase. The two generated phase values are then utilized to reconstruct the 3D information. During the training process, we use a refined reference phase which utilizes fringe images with different frequencies. SFNet has the capability to complement the additional frequency information by fusing the feature maps extracted from each encoder. Comparative experiments and ablation studies validate the effectiveness of our proposed method. The dataset is publicly accessible on our project page https://wonhoe-kim.github.io/SFNet/. [Display omitted] • We propose a 3D reconstruction framework utilizing deep learning based on Fringe Projection Profilometry. • SFNet is designed for phase retrieval only using two images. • The proposed method achieves accurate predictions of absolute phase with high precision. • We present a large-scale dataset for FPP technique within a virtual environment. [ABSTRACT FROM AUTHOR]

This Letter introduces a novel, to the best of our knowledge, calibration method for structured light systems that simplifies the calibration process and reduces time consumption. We combine vertical and horizontal fringe patterns into a single composite pattern and retrieve the bidirectional phase based on Fourier transform profilometry (FTP). The entire calibration process becomes faster and more simplified by capturing only a single-shot pattern. Experimental results demonstrate that the proposed method achieves high accuracy and robustness, comparable to the conventional method that requires capturing numerous fringe patterns for the typical phase-shifting algorithm.