Augmented reality has a high potential in interior design due to its capability of visualizing numerous prospective designs directly in a target room. In this paper, we present our research on utilization of augmented reality for interactive and personalized furnishing. We propose a new algorithm for automated interior design which generates sensible and personalized furniture configurations. This algorithm is combined with mobile augmented reality system to provide a user with an interactive interior design try-out tool. Personalized design is achieved via a recommender system which uses user preferences and room data as input. We conducted three user studies to explore different aspects of our research. The first study investigated the user preference between augmented reality and on-screen visualization for interactive interior design. In the second user study, we studied the user preference between our algorithm for automated interior design and optimization-based algorithm. Finally, the third study evaluated the probability of sensible design generation by the compared algorithms. The main outcome of our research suggests that augmented reality is viable technology for interactive home furnishing.

A recent trend in interactive environments is the use of unstructured and temporally varying point clouds. This is driven by both affordable depth cameras and augmented reality simulations. One research question is how to perform collision detection on such point clouds. State-of-the-art methods for collision detection create a spatial hierarchy in order to capture dynamic point cloud surfaces, but they require O(NlogN) time for N points. We propose a novel screen-space representation for point clouds which exploits the property of the underlying surface being 2D. In order for dimensionality reduction, a 3D point cloud is converted into a series of thickened layered depth images. This data structure can be constructed in O(N) time and allows for fast surface queries due to its increased compactness and memory coherency. On top of that, parts of its construction come for free since they are already handled by the rendering pipeline. As an application we demonstrate online collision detection between dynamic point clouds. It shows superior accuracy when compared to other methods and robustness to sensor noise since uncertainty is hidden by the thickened boundary.

Large-scale unstructured point cloud scenes can be quickly visualized without prior reconstruction by utilizing levels-of-detail structures to load an appropriate subset from out-of-core storage for rendering the current view. However, as soon as we need structures within the point cloud, e.g., for interactions between objects, the construction of state-of-the-art data structures requires O(NlogN) time for N points, which is not feasible in real time for millions of points that are possibly updated in each frame. Therefore, we propose to use a surface representation structure which trades off the (here negligible) disadvantage of single-frame use for both output-dominated and near-linear construction time in practice, exploiting the inherent 2D property of sampled surfaces in 3D. This structure tightly encompasses the assumed surface of unstructured points in a set of bounding depth intervals for each cell of a discrete 2D grid. The sorted depth samples in the structure permit fast surface queries, and on top of that an occlusion graph for the scene comes almost for free. This graph enables novel real-time user operations such as revealing partially occluded objects, or scrolling through layers of occluding objects, e.g., walls in a building. As an example application we showcase a 3D scene exploration framework that enables fast, more sophisticated interactions with point clouds rendered in real time.