Reconstructing real object appearance with virtual materials using mobile augmented reality

In augmented reality (AR) applications, detecting and tracking real-world objects remains a challenge. Another challenge is making the loaded virtual objects truly reflect the lighting and shadow conditions of the actual environment in which they are located. In this paper, we proposed an improved method for real-time light estimation of environmental lighting in AR applications, tracking the movement of physical objects, and making the corresponding virtual objects reflect the lighting and shadows of the actual environment. First, we proposed an effective method for estimating light in an AR scene by training a deep neural network once on different scenes from the input of RGB-D images to determine the direction of the brightest light. Second, we experimented with advanced methods of detecting and tracking different types of real objects (flat surfaces, simple geometries, and complex geometries) for visualizing virtual materials on the top of these surfaces, where virtual materials will acquire the conditions of the real place in terms of lighting, reflection, and shadows depending on our proposed method for light estimation. Our method of light estimation was tested via experiments on the three types of real objects mentioned above. Results indicate that our method achieves higher resolution and lower error rate compared with other work. Our methods have achieved very good results in terms of detection accuracy, tracking accuracy, realistic visualization of materials, matching real shade, and lighting conditions.