Shape Complexity from Image Similarity

We present an approach to automatically compute the complexity of a given 3D shape. Previous approaches have made use of geometric and/or topological properties of the 3D shape to compute complexity. Our approach is based on shape appearance and estimates the complexity of a given 3D shape according to how 2D views of the shape diverge from each other. We use similarity among views of the 3D shape as the basis for our complexity computation. Hence our approach uses claims from psychology that humans mentally represent 3D shapes as organizations of 2D views and, therefore, mimics how humans gauge shape complexity. Experimental results show that our approach produces results that are more in agreement with the human notion of shape complexity than those obtained using previous approaches

Novel Methods and Algorithms for Presenting 3D Scenes

In recent years, improvements in the acquisition and creation of 3D models gave rise to an increasing availability of 3D content and to a widening of the audience such content is created for, which brought into focus the need for effective ways to visualize and interact with it. Until recently, the task of virtual inspection of a 3D object or navigation inside a 3D scene was carried out by using human machine interaction (HMI) metaphors controlled through mouse and keyboard events. However, this interaction approach may be cumbersome for the general audience. Furthermore, the inception and spread of touch-based mobile devices, such as smartphones and tablets, redefined the interaction problem entirely, since neither mouse nor keyboards are available anymore. The problem is made even worse by the fact that these devices are typically lower power if compared to desktop machines, while high-quality rendering is a computationally intensive task. In this thesis, we present a series of novel methods for the easy presentation of 3D content both when it is already available in a digitized form and when it must be acquired from the real world by image-based techniques. In the first case, we propose a method which takes as input the 3D scene of interest and an example video, and it automatically produces a video of the input scene that resembles the given video example. In other words, our algorithm allows the user to replicate an existing video, for example, a video created by a professional animator, on a different 3D scene. In the context of image-based techniques, exploiting the inherent spatial organization of photographs taken for the 3D reconstruction of a scene, we propose an intuitive interface for the smooth stereoscopic navigation of the acquired scene providing an immersive experience without the need of a complete 3D reconstruction. Finally, we propose an interactive framework for improving low-quality 3D reconstructions obtained through image-based reconstruction algorithms. Using few strokes on the input images, the user can specify high-level geometric hints to improve incomplete or noisy reconstructions which are caused by various quite common conditions often arising for objects such as buildings, streets and numerous other human-made functional elements

On Computing Best Fly

With growing popularity of online 3D shape databases, the problem of navigation and remote visualisation of large 3D shape models in such repositories is gaining prominence. While some recent work has focused on automatically computing the best view(s) of a given model, little attention has been given to the problem's dynamic counterpart - best fly. In this paper, we propose a solution to this problem that extends on previous best view methods. Given a shape, we use its best views to compute a path on its viewsphere which acts as a trajectory for a virtual camera pointing at the object. We then use the model's geometric properties to determine the speed and zoom of the camera along the path

On Computing Best Fly

With growing popularity of online 3D shape databases, the problem of navigation and remote visualisation of large 3D shape models in such repositories is gaining prominence. While some recent work has focused on automatically computing the best view(s) of a given model, little attention has been given to the problem's dynamic counterpart - best fly. In this paper, we propose a solution to this problem that extends on previous best view methods. Given a shape, we use its best views to compute a path on its viewsphere which acts as a trajectory for a virtual camera pointing at the object. We then use the model's geometric properties to determine the speed and zoom of the camera along the path