Haptic rendering of continuous parametric models

Haptic rendering is the process of computing restoring forces that are required to generate a perception of touch between a user and a virtual environment. The realism of haptic rendering depends mainly on haptic rendering algorithms and the modeling of virtual objects in a virtual environment. Friction and texture rendering also play an important role in increasing the realism of the experience between a user and a virtual environment. The state of the art haptic and friction rendering algorithms in the literature are developed for polygonal models. These approaches can not benefit from the advantages of continuous parametric surfaces such as compact representation, higher order continuity and exact computation of surface normals. In this thesis, a feedback-stabilized closest point tracking based haptic rendering algorithm is extended by introducing a direct friction rendering method for continuous parametric surfaces. Unlike the existing approaches, the proposed friction rendering method is direct and does not rely on the algorithms introduced for polyhedral surfaces. This algorithm implements the stiction model of friction for haptic rendering of parametric surfaces. It can directly operate on parametric models and can handle surfaces with high curvature. Furthermore, the algorithm allows transitions from sticking to sliding and sliding to sticking, as well as surface to surface transitions, without introducing discontinuous force artifacts. The algorithm also allows for tuning of the friction coefficient during the mode transitions to enable rendering of the Stribeck effect. Thanks to its feedback-stabilized core, it is robust against drift and numerical noise. The algorithm is computationally efficient (with respect to time and space); its applicability and effectiveness to simulate friction are verified through simulations and real-time implementations. In particular, the friction rendering algorithm is tested using pre-determined trajectories that demonstrate successful rendering of static friction at a corner, the mode changes from static-to-dynamic and dynamic-to-static friction

Joint Material and Illumination Estimation from Photo Sets in the Wild

Faithful manipulation of shape, material, and illumination in 2D Internet images would greatly benefit from a reliable factorization of appearance into material (i.e., diffuse and specular) and illumination (i.e., environment maps). On the one hand, current methods that produce very high fidelity results, typically require controlled settings, expensive devices, or significant manual effort. To the other hand, methods that are automatic and work on 'in the wild' Internet images, often extract only low-frequency lighting or diffuse materials. In this work, we propose to make use of a set of photographs in order to jointly estimate the non-diffuse materials and sharp lighting in an uncontrolled setting. Our key observation is that seeing multiple instances of the same material under different illumination (i.e., environment), and different materials under the same illumination provide valuable constraints that can be exploited to yield a high-quality solution (i.e., specular materials and environment illumination) for all the observed materials and environments. Similar constraints also arise when observing multiple materials in a single environment, or a single material across multiple environments. The core of this approach is an optimization procedure that uses two neural networks that are trained on synthetic images to predict good gradients in parametric space given observation of reflected light. We evaluate our method on a range of synthetic and real examples to generate high-quality estimates, qualitatively compare our results against state-of-the-art alternatives via a user study, and demonstrate photo-consistent image manipulation that is otherwise very challenging to achieve

Review of the mathematical foundations of data fusion techniques in surface metrology

The recent proliferation of engineered surfaces, including freeform and structured surfaces, is challenging current metrology techniques. Measurement using multiple sensors has been proposed to achieve enhanced benefits, mainly in terms of spatial frequency bandwidth, which a single sensor cannot provide. When using data from different sensors, a process of data fusion is required and there is much active research in this area. In this paper, current data fusion methods and applications are reviewed, with a focus on the mathematical foundations of the subject. Common research questions in the fusion of surface metrology data are raised and potential fusion algorithms are discussed