Co-located haptic feedback in mixed and augmented reality environments can improve realism and user performance, but it also requires careful system design and calibration. In this poster, we determine the thresholds for perceiving co-location errors through two psychophysics experiments in a typical fine-motor manipulation task. In these experiments we simulate the two fundamental ways of implementing VHAR systems: first, attaching a real tool; second, augmenting a virtual tool. We determined the just-noticeable co-location errors for position and orientation in both experiments and found that users are significantly more sensitive to co-location errors with virtual tools. Our overall findings are useful for designing visuo-haptic augmented reality workspaces and calibration procedures.

The goal of our work is to create highly realistic graphics for Augmented Reality on mobile phones. One of the greatest challenges for this is to provide realistic lighting of the virtual objects that matches the real world lighting. This becomes even more difficult with the limited capabilities of mobile phone GPUs. Our approach differs in the following important aspects compared to previous attempts: (1) most have relied on rasterizer approaches, while our approach is based on raytracing; (2) we perform distributed rendering in order to address the limited mobile GPU capabilities; (3) we use image-based lighting from a pre-captured panorama to incorporate real world lighting. We utilize two markers: one for object tracking and one for registering the panorama. Our initial results are encouraging, as the visual quality resembles real objects and also the reference renderings which were created offline. However, we still need to validate our approach in human subject studies, especially with regards to the trade-off between latency of remote rendering and visual quality.

ClonAR enables users to rapidly clone and edit real-world objects. First, real-world objects can be scanned using KinectFusion. Second, users can edit the scanned objects in our visuo-haptic Augmented Reality environment. Our whole pipeline does not use mesh representation of objects, but rather Signed Distance Fields, which are the output of KinectFusion. We directly render Signed Distance Fields haptically and visually. We do direct haptic rendering of Signed Distance Fields, which is faster and more flexible than rendering meshes. Visual rendering is performed by our custom-built raymarcher, which facilitates realistic illumination effects like ambient occlusions and soft shadows. Our prototype demonstrates the whole pipeline. We further present several results of redesigned real-world objects.