基 于 平 板 投 影 显 示 与 视 点 跟 踪 的 光 学 伪 装 技 术

Objective In fields such as military and public security, it is often necessary to camouflage or make devices and facilities invisible within a given scene, which makes it difficult for specific observers to notice them. Therefore, there is a need to develop optical camouflage or stealth equipment for military and public security applications. Common camouflage gear, such as camouflage clothing, often lacks adaptability to environmental changes. Advanced techniques for achieving better camouflage effects include optical camouflage and optical invisibility (optical cloaking) technologies. Optical invisibility refers to the cloaking design based on transformation optics, which, in theory, can achieve an ideal invisible effect. However, due to material limitations, its application in the visible spectrum and for large objects remains challenging. Optical camouflage relies on the assumption that observers perceive the world based on a perspective projection model and are insensitive to depth perception. By capturing background images and generating camouflage images based on the observer’s viewpoint, this technique creates a misleading effect that confuses the observer. To design a flexible and practical camouflage system, we study optical camouflage technology based on flat panel projections and viewpoint tracking. Based on the spatial geometric principles of optical projection camouflage, we propose a method for generating camouflage images. Furthermore, we complete the design, construction, and calibration of the prototype system, achieving an initial dynamic camouflage effect. Methods The principle and method of the optical camouflage system based on flat-panel projection displays and viewpoint tracking can be interpreted as a spatial multi-view geometry problem. Previous research focuses on scenarios where the background environment is a flat plane, and the observer is a fixed camera with calibrated intrinsic and extrinsic parameters. Multi-view geometry methods are applied to analyze the system structure and camouflage image generation methods. We extend the analysis and provide a mathematical formula to calculate the corresponding camouflage points for background points captured by the background camera. In the perspective projection of the observer’s view, the background point and the camouflage point appear at the same position. The camouflage point is located in front of the background point, creating a camouflage effect for the areas behind the camouflage point. Based on the physical model, we refine the working principle of camouflage technology based on flat panel projections and viewpoint tracking and develop a new camouflage image generation algorithm. We design and implement a prototype, and experiments are conducted in a laboratory setting, achieving initial dynamic camouflage effects. Compared with the method based on edge point estimation and clipping to generate camouflage images, the effectiveness of the proposed method is verified. Results and Discussions We demonstrate that calculating the camouflage points only requires the coordinates of the observer’s position, without the need for the intrinsic and extrinsic parameters of the observer as a perspective projection camera. It implies that optical camouflage can be achieved solely by detecting the position of the observer. The structure of the camouflage system is designed using a depth camera instead of background plane calibration or estimation, which enables camouflage for environments with varying depths. By using a camera to detect the position of the observer in real-time, dynamic camouflage for a moving observer can be realized. Additionally, a camouflage image generation algorithm based on camouflage point culling, rasterization, and depth buffering is designed to reduce image defects when the camouflage points are directly used as the camouflage image. Finally, a prototype of the optical camouflage system has been developed, including the background camera set, observer detection camera set, camouflage screen, and computing host. The camera set utilizes stereo depth cameras. The calibration algorithm is used to ensure that the input system structure parameters are correct. In particular, an algorithm is designed for the calibration of the camera set relative to the camouflage screen located outside its field of view. The experimental prototype achieves a more accurate camouflage effect than previous methods. Defects in the camouflaged image can be mitigated by background modeling or high-precision depth cameras. Conclusions The optical camouflage technology based on flat panel projections and viewpoint tracking can achieve camouflage for observers conforming to perspective projection. In this paper, we demonstrate the possibility of camouflaging a depth-variable background for a movable observer and propose a method for camouflage device design. The calculation process for camouflage images requires the color and depth images of the background environment, as well as the 3D coordinates of the observer. Therefore, the background cameras and observer detection devices should be depth cameras or have depth detection capabilities. Additionally, the relative positions between the camouflage screen and the cameras need to be calibrated in advance. The camouflage device captures background images and detects the position of the observer to generate and display a camouflage image on the screen, which ensures that from the observer’s perspective, the camouflage image appears similar to the real background. This verifies that the camouflage system based on display devices and observer detection can achieve effective camouflage visual effects. A“camouflage box”suitable for practical applications can be achieved by using flexible display screens.