# Research on Panorama Generation from a Multi-Camera System by Object-Distance Estimation
## 1 Introduction  
In recent years, panoramic images have been extensively employed in ground remote sensing applications, including street image acquisition, traffic monitoring, virtual reality, robot navigation and mobile mapping applications, etc. [1; 2; 3; 4; 5]. At present, there are mainly two types of panoramic imaging methods on the market, namely catodaptric imaging and multi-camera imaging methods. The catodaptric panoramic method uses a plane mirror to refract the surrounding light to a single camera [6; 7; 8; 9]. Due to their complex imaging mechanisms and manufacturing processes, catadoptric panoramic systems have been less commonly used compared to the second methods. With the development of sensors and computing abilities, many panoramic camera systems have been developed by a combination of multiple cameras [10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24], which can be divided into two categories: monocular panorama systems and binocular panorama systems. Although a binocular panorama system is able to provide the 3D scene information needed for seamless panorama generation and 3D reconstruction, it requires higher complexity of system design and processing than that of the monocular panorama system [25]. Therefore, the development of monocular panorama systems using multiple cameras and the generation of accurate panoramic images remain crucial in the fields of remote sensing and computer vision applications. In recent years, several monocular panoramic imaging systems based on multiple cameras have been developed. Among these, professional panoramic systems often incorporate multiple cameras, such as Point Grey's Ladybug series cameras [10; 11], Google'svehicle NCTech [12], Leica's mobile panoramic cameras [13], and more. To achieve precise panorama generation with these integrated systems, relevant research can be categorized into two main areas. The first approach is feature point-based algorithms [19; 20; 21; 22; 23]. Various tools have been developed in the multimedia industry for panorama creation, such as Color Autopano and Autostitch [19], among others. Panoramas are generated based on the second method, which relies on a panoramic imaging model derived from the pre-calibrated geometric relationships between cameras. Both the ideal and rigorous panoramic spherical and cylindrical imaging models have been proposed [24; 25; 26; 27; 28] and successfully applied for vehicle-based remote sensing mapping and panoramic SLAM 3D reconstruction, particularly with the professional panoramic camera LadyBug3 [25]. However, it is important to mention that both the ideal and rigorous panoramic imaging models are developed under the assumption of constant object-distance information in the surroundings. In order to meet the requirements of accurate panoramic mapping and 3D reconstruction applications, the accuracy and robustness of virtual panorama still need to be improved by embodying the varied object-distance information into a mosaicking imaging scheme. In this case, it is a vital challenge to accurately estimate the object-distance information of the surrounding scene. In addition, problems such as empty pixels and pixel aggregation in the virtual panoramas generated by the direct panoramic imaging models may still exist. This paper presents a panorama generation scheme and the advanced technologies used for a monocular panoramic system. The primary contributions of this work lie in three sides:  
1. In contrast to expensive panoramic camera systems that rely on professional cameras, a low-cost panoramic camera system is developed by a combination of eight low-cost web cameras in this paper. 2. An inverse rigorous panoramic imaging model is derived completely based on the pre-calibration of the inner orientation elements, distortion parameters of each camera, and the relative orientation relationships between cameras. 3. A scheme is proposed to estimate object-distance information within a surrounding scene.