Rafael Kuffner dos Anjos

Siddhant Prakash, David R. Walton, Rafael K. dos Anjos et al.

Real camera footage is subject to noise, motion blur (MB) and depth of field (DoF). In some applications these might be considered distortions to be removed, but in others it is important to model them because it would be ineffective, or interfere with an aesthetic choice, to simply remove them. In augmented reality applications where virtual content is composed into a live video feed, we can model noise, MB and DoF to make the virtual content visually consistent with the video. Existing methods for this typically suffer two main limitations. First, they require a camera calibration step to relate a known calibration target to the specific cameras response. Second, existing work require methods that can be (differentiably) tuned to the calibration, such as slow and specialized neural networks. We propose a method which estimates parameters for noise, MB and DoF instantly, which allows using off-the-shelf real-time simulation methods from e.g., a game engine in compositing augmented content. Our main idea is to unlock both features by showing how to use modern computer vision methods that can remove noise, MB and DoF from the video stream, essentially providing self-calibration. This allows to auto-tune any black-box real-time noise+MB+DoF method to deliver fast and high-fidelity augmentation consistency.

Rafael Kuffner dos Anjos, João Madeiras Pereira, José Antonio Gaspar

Three-dimensional reconstruction of events recorded on images has been a common challenge between computer vision and computer graphics for a long time. Estimating the real position of objects and surfaces using vision as an input is no trivial task and has been approached in several different ways. Although huge progress has been made so far, there are several open issues to which an answer is needed. The use of videos as an input for a rendering process (video-based rendering, VBR) is something that recently has been started to be looked upon and has added many other challenges and also solutions to the classical image-based rendering issue (IBR). This article presents the state of art on video-based rendering and image-based techniques that can be applied on this scenario, evaluating the open issues yet to be solved, indicating where future work should be focused.

Maurício Sousa, Daniel Mendes, Rafael Kuffner dos Anjos et al.

Face-to-face telepresence promotes the sense of "being there" and can improve collaboration by allowing immediate understanding of remote people's nonverbal cues. Several approaches successfully explored interactions with 2D content using a see-through whiteboard metaphor. However, with 3D content, there is a decrease in awareness due to ambiguities originated by participants' opposing points-of-view. In this paper, we investigate how people and content should be presented for discussing 3D renderings within face-to-face collaborative sessions. To this end, we performed a user evaluation to compare four different conditions, in which we varied reflections of both workspace and remote people representation. Results suggest potentially more benefits to remote collaboration from workspace consistency rather than people's representation fidelity. We contribute a novel design space, the Negative Space, for remote face-to-face collaboration focusing on 3D content.