This paper presents a method for localizing primitive shapes in a dense point cloud computed by the RGB-D SLAM system. To stably generate a shape map containing only primitive shapes, the primitive shape is incrementally modeled by fusing the shapes estimated at previous frames in the SLAM, so that an accurate shape can be finally generated. Specifically, the history of the fusing process is used to avoid the influence of error accumulation in the SLAM. The point cloud of the shape is then updated by fusing the points in all the previous frames into a single point cloud. In the experimental results, we show that metric primitive modeling in texture-less and unprepared environments can be achieved online.

Finding high-level semantic information from a point cloud is a challenging task, and it can be used in various applications. For instance, it is useful to compactly represent the scene structure and efficiently understand the scene context. This task is even more challenging when using a hand-held monocular visual SLAM system that outputs a noisy sparse point cloud. In order to tackle this issue, we propose an incremental primitive modeling method using both geometric and statistical analyses for such point cloud. The main idea is to select only reliably-modeled shapes by analyzing the geometric relationship between the point cloud and the estimated shapes. Besides that, a statistical evaluation is incorporated to filter wrongly-detected primitives in a noisy point cloud. As a result of this processing, our approach largely improved precision when compared with state of the art methods. We also show the impact of segmenting and representing a scene using primitives instead of a point cloud.

Augmented Reality Authoring Tools are important instruments that can help a widespread use of AR. They can be classified as programming or content design tools in which the latter completely removes the necessity of programming skills to develop an AR solution. Several solutions have been developed in the past years, however there are few works aiming to identify patterns and general models for such tools. This work aims to perform a trend analysis on content design tools in order to identify their functionalities regarding AR, authoring paradigms, deployment strategies and general dataflow models. This work is aimed to assist developers willing to create authoring tools, therefore, it focus on the last three aspects. Thus, 19 tools were analyzed and through this evaluation it were identified two authoring paradigms and two deployment strategies. Moreover, from their combination it was possible to elaborate four generic dataflow models in which every tool could be fit into.

According to Gartner's 2013 Hype Cycle for Emerging Technologies, Augmented Reality will reach its Plateu of Productivity before Wearable User Interfaces. In this work, device and user-perspective rendering are compared regarding their applicability to AR-based solutions for Google Glass. The conducted experiment measured and evaluated the advantages and drawbacks on each method and also got positive and negative feedbacks given by users. The tests showed that users preferred the device-perspective approach.

This demonstration allows visitors to use AR applications for monocular optical see-through head-mounted displays with two forms of visualization. One is the device-perspective approach, in which the user sees the virtual content registered with the camera image at the display. The other is the user-perspective method, in which the display is used as a de facto optical see-through device and the virtual content is registered with the real world.

In this work in progress we address the problem of interacting with augmented objects. A bare hand tracking technique is developed, which allied to gesture recognition heuristics, enables interaction with augmented objects in an intuitive way. The tracking algorithm uses a flock of features approach that tracks both hands in real time. The interaction occurs by the execution of grasp and release gestures. Physics simulation and photorealistic rendering are added to the pipeline. This way, the tool provides more coherent feedback in order to make the virtual objects look and respond more likely real ones. The pipeline was tested through specific tasks, designed to analyze its performance regarding the easiness of use, precision and response time.

This demonstration will allow visitors to use different applications builded for the ARBlocks, a dynamic blocks platform based on projective augmented reality and tangible user interfaces aiming early childhood educational activities. Those applications, along with the platform itself, were designed to be useful tools for educators to teach general subjects for children, such as mathematical and language skills, as well as develop important abilities, like motor coordination and collaboration.