High-efficiency infrared 3D reconstruction with hybrid SFM-MVS for large scenes

Traditional multi-view three-dimensional (3D) reconstruction uses images from visible light that may be of poor quality under conditions such as reflections, light spots, or haze, causing difficulties for 3D reconstruction in large scenes. To solve this problem, an efficient hybrid structure in motion (SFM) and multi-view stereo (MVS) point cloud reconstruction and alignment fusion method based on infrared images is proposed in this paper. Since thermal infrared imaging is less affected by lighting conditions and smoke, we adopt infrared images as the reconstruction raw dataset to make up for the defects of visible light 3D reconstruction; the reconstruction method adopts the hybrid SFM-MVS algorithm reconstruction with subset partitioning strategy, which can complete the dense reconstruction more efficiently under a large amount of image data. The iterative closest point (ICP) algorithm is used to perform the fusion of multi-angle reconstruction for the alignment of the point cloud, which can make up for the lack of side information in the large scene model reconstructed from a single angle. The experiment finally obtained a fairly good infrared 3D scene model with complete side information.