Robust structured light 3D imaging with two fringe patterns using recurrent classification neural network

Abstract

Robust and accurate 3D reconstruction using a limited number of fringe patterns has posed a challenge in the field of structured light 3D imaging. Unlike traditional approaches that rely on multiple fringe patterns, using only one or two patterns makes phase recovery and unwrapping difficult. To address this issue, a recurrent classification neural network (RCNN) has been developed, transforming the phase recovery and unwrapping tasks into a unified phase classification task. First, a training dataset consisting of 1,200 groups of data was collected to generate a total of 38,400 training samples, enabling the RCNN to learn the mapping between the input fringe patterns and the corresponding label maps. Then, based on the well-trained network, a label map is generated based on the input two fringe patterns using the output classification results. Finally, 3D reconstruction data could be obtained by combining the inferred label map with the vision system's parameters. A series of comprehensive experiments have been conducted to validate the performance of the proposed method.

360 degree unique and robust matching method for the infrared optical features of handheld target

“…2. Keep the first copy of data unchanged and extract (2,3) and (4,5) from the second copy of data, dividing them into two arrays, i.e., (2,3) and (4,5). Then, arrange these two arrays in ascending order by the y-axis.…”

“…1 Differing from structured light-based stereo vision 3D reconstruction techniques, the handheld optical marker measurement method is fundamentally a passive 3D measurement approach. 2,3 Therefore, ensuring the uniqueness and robustness of marker point matching is of paramount importance when conducting 3D measurements. However, existing methods often exhibit certain limitations in designing algorithms, making it challenging to guarantee the uniqueness and robust matching of marker points under free 360-degree target orientations.…”