Infrared target 3D reconstruction based on TRC-MVSNet

Abstract

A 3D reconstruction network, TRC-MVSNet, is proposed to address the issue of poor 3D reconstruction of infrared multi-view. It combines Transformer feature matching and RC-MVSNet. To enhance the quality of the infrared image, a moving average filter method is employed to mitigate noise by averaging signals in a sliding window. Additionally, the Transformer network is utilized to improve the matching quality of infrared multi-view information by matching the information of multiple infrared views in the feature matching stage. Experimental results demonstrate that the TRC-MVSNet 3D reconstruction model enhances accuracy by 0.03 and synthesis by 0.013 compared to the RC-MVSNet 3D reconstruction model on the DTU dataset. The TRC-MVSNet achieves high accuracy, efficiency, and minimal noise in the 3D reconstruction of infrared targets on the SYLU-IMD dataset, outperforming other methods.