Real-time panoramic map modeling method based on multisource image fusion and three-dimensional rendering

Abstract

.Under the complex environment, in the process of sensing and understanding the surrounding scene with an unmanned system, the traditional single-source panorama provides limited information and is prone to interference from lighting, it is difficult to capture hidden objects, and the user’s three-dimensional (3D) experience is poor. To solve this problem, we propose a real-time panoramic map modeling method based on multisource image fusion and 3D rendering with intensity, hue, saturation transform and wavelet technology, open graphics library. Five general image sharpness evaluation methods are used to analyze the single-source and multisource close-range and distant images during the day and night. The results show that in the daytime, the sharpness of the multisource image is significantly improved compared with the single-source visible light image, especially the energy gradient evaluation can reach 3.4 times. Similarly, compared with the single-source infrared image, the multisource image at night also improve significantly, the energy gradient evaluation can reach 1.4 times. This conclusion is verified by the subsequent experiments of several indoor scenes, the 3D rendering processing technology of panoramic map is realized, which improves user experience of 3D stereoscopic vision, and will provide multisource image fusion and 3D rendering technical support for unmanned real-time panoramic map modeling.

Infrared and visible image fusion method based on hierarchical attention mechanism

“…In the category of traditional image fusion algorithms, multiscale transformation methods have been widely used to extract multiscale features from source images, and feature channels are combined through appropriate fusion strategies. [5][6][7][8] Finally, the fused image is reconstructed through inverse multiscale transformation. However, the fusion performance of these algorithms highly depends on the feature extraction methods used.…”

“…Currently, image fusion techniques have made significant progress. In the category of traditional image fusion algorithms, multiscale transformation methods have been widely used to extract multiscale features from source images, and feature channels are combined through appropriate fusion strategies 5 – 8 Finally, the fused image is reconstructed through inverse multiscale transformation.…”