Make it home

TL;DR: In this article, a method to realistically insert synthetic objects into existing photographs without requiring access to the scene or any additional scene measurements is proposed, which can be used for home decorating and user content creation.

TL;DR: This work focuses its attention on depth based semantic per-pixel labelling as a scene understanding problem and shows the potential of computer graphics to generate virtually unlimited labelled data from synthetic 3D scenes.

TL;DR: An interactive approach to semantic modeling of indoor scenes with a consumer-level RGBD camera, which takes an RGBD image of an indoor scene, which is automatically segmented into a set of regions with semantic labels.

TL;DR: In this paper, the authors discuss the challenges and solutions of applying VR training to different application domains, such as first responder training, medical training, military training, workforce training, and education, and discuss possible future directions to leverage VR technology advances for developing novel training experiences.

TL;DR: The analogy between images and statistical mechanics systems is made and the analogous operation under the posterior distribution yields the maximum a posteriori (MAP) estimate of the image given the degraded observations, creating a highly parallel ``relaxation'' algorithm for MAP estimation.

TL;DR: A generalization of the sampling method introduced by Metropolis et al. as mentioned in this paper is presented along with an exposition of the relevant theory, techniques of application and methods and difficulties of assessing the error in Monte Carlo estimates.

TL;DR: A detailed, introductory exposition of the Metropolis-Hastings algorithm, a powerful Markov chain method to simulate multivariate distributions, and a simple, intuitive derivation of this method is given along with guidance on implementation.

TL;DR: To facilitate use of the Bayes factor, an easy-to-use, Web-based program is provided that performs the necessary calculations and has better properties than other methods of inference that have been advocated in the psychological literature.