This paper presents the augmentation of immersive omnidirectional video with realistically lit objects. Recent years have known a proliferation of real-time capturing and rendering methods of omnidirectional video. Together with these technologies, rendering devices such as Oculus Rift have increased the immersive experience of users. We demonstrate the use of structure from motion on omnidirectional video to reconstruct the trajectory of the camera. The position of the car is then linked to an appropriate 360 • environment map. State-of-the-art augmented reality applications have often lacked realistic appearance and lighting. Our system is capable of evaluating the rendering equation in real-time, by using the captured omnidirectional video as a lighting environment. We demonstrate an application in which a computer generated vehicle can be controlled through an urban environment.
In this paper a deep learning architecture is presented that can, in real time, detect the 2D locations of certain landmarks of physical tools, such as a hammer or screwdriver. To avoid the labor of manual labeling, the network is trained on synthetically generated data. Training computer vision models on computer generated images, while still achieving good accuracy on real images, is a challenge due to the difference in domain. The proposed method uses an advanced rendering method in combination with transfer learning and an intermediate supervision architecture to address this problem. It is shown that the model presented in this paper, named Intermediate Heatmap Model (IHM), generalizes to real images when trained on synthetic data. To avoid the need for an exact textured 3D model of the tool in question, it is shown that the model will generalize to an unseen tool when trained on a set of different 3D models of the same type of tool. IHM is compared to two existing approaches to keypoint detection and it is shown that it outperforms those at detecting tool landmarks, trained on synthetic data.
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