A 3D Reconstruction of Terahertz Images Based on the FCTMVSNet Algorithm

The terahertz range, as a type of electromagnetic wave with wavelengths between microwaves and the infrared band, has the characteristics of penetration, low energy and a stable absorption spectrum of specific substances, and is widely used in non-destructive testing, human security inspections, biological tissue diagnoses and military detection. In particular, terahertz wave 3D imaging technology can detect the internal information of the target of detection, and it has become the focus of current research. This study carried out research on 3D reconstruction and object detection algorithms based on terahertz images. In view of the problem that the MVS (Multi-ViewStereo) series of 3D reconstruction algorithms ignore the context information between the cost layers and have unsatisfactory reconstruction effects when used on complex regions, an improved MVSNet 3D reconstruction algorithm FCTMVSNet(Feature and Cost Transformer Depth Inference for Unstructured Multi-view Stereo) based on Transformer is proposed here. A structured object recognition algorithm was designed to provide theoretical support for subsequent terahertz image-based object detection algorithms.