Narrative visualization with augmented reality

The following study addresses, from a design perspective, narrative visualization using augmented reality (AR) in real physical spaces, and specifically in spaces with no semantic relation with the represented data. We intend to identify the aspects augmented reality adds, as narrative possibilities, to data visualization. Particularly, we seek to identify the aspects augmented reality introduces regarding the three dimensions of narrative visualization—view, focus and sequence. For this purpose, we adopted a comparative analysis of a set of fifty case studies, specifically, narrative visualizations using augmented reality from a journalistic scope, where narrative is a key feature. Despite the strong explanatory character that characterizes the set of analyzed cases, which sometimes limits the user’s agency, there is a strong interactive factor. It was found that augmented reality can expand the narrative possibilities in the three dimensions mentioned—view, focus and sequence—but especially regarding visual strategies where simulation plays an essential role. As a visual strategy, simulation can provide the context for communication or be the object of communication itself, as a replica.publishe

Gaussian belief propagation for real-time decentralised inference

For embodied agents to interact intelligently with their surroundings, they require perception systems that construct persistent 3D representations of their environments. These representations must be rich; capturing 3D geometry, semantics, physical properties, affordances and much more. Constructing the environment representation from sensory observations is done via Bayesian probabilistic inference and in practical systems, inference must take place within the power, compactness and simplicity constraints of real products. Efficient inference within these constraints however remains computationally challenging and current systems often require heavy computational resources while delivering a fraction of the desired capabilities. Decentralised algorithms based on local message passing with in-place processing and storage offer a promising solution to current inference bottlenecks. They are well suited to take advantage of recent rapid developments in distributed asynchronous processing hardware to achieve efficient, scalable and low-power performance. In this thesis, we argue for Gaussian belief propagation (GBP) as a strong algorithmic framework for distributed, generic and incremental probabilistic estimation. GBP operates by passing messages between the nodes on a factor graph and can converge with arbitrary asynchronous message schedules. We envisage the factor graph being the fundamental master environment representation, and GBP the flexible inference tool to compute local in-place probabilistic estimates. In large real-time systems, GBP will act as the `glue' between specialised modules, with attention based processing bringing about local convergence in the graph in a just-in-time manner. This thesis contains several technical and theoretical contributions in the application of GBP to practical real-time inference problems in vision and robotics. Additionally, we implement GBP on novel graph processor hardware and demonstrate breakthrough speeds for bundle adjustment problems. Lastly, we present a prototype system for incrementally creating hierarchical abstract scene graphs by combining neural networks and probabilistic inference via GBP.Open Acces

Enhanced tracking and recognition of moving objects by reasoning about spatio-temporal continuity.

A framework for the logical and statistical analysis and annotation of dynamic scenes containing occlusion and other uncertainties is presented. This framework consists of three elements; an object tracker module, an object recognition/classification module and a logical consistency, ambiguity and error reasoning engine. The principle behind the object tracker and object recognition modules is to reduce error by increasing ambiguity (by merging objects in close proximity and presenting multiple hypotheses). The reasoning engine deals with error, ambiguity and occlusion in a unified framework to produce a hypothesis that satisfies fundamental constraints on the spatio-temporal continuity of objects. Our algorithm finds a globally consistent model of an extended video sequence that is maximally supported by a voting function based on the output of a statistical classifier. The system results in an annotation that is significantly more accurate than what would be obtained by frame-by-frame evaluation of the classifier output. The framework has been implemented and applied successfully to the analysis of team sports with a single camera. Key words: Visua

Compute-Bound and Low-Bandwidth Distributed 3D Graph-SLAM

This article describes a new approach for distributed 3D SLAM map building. The key contribution of this article is the creation of a distributed graph-SLAM map-building architecture responsive to bandwidth and computational needs of the robotic platform. Responsiveness is afforded by the integration of a 3D point cloud to plane cloud compression algorithm that approximates dense 3D point cloud using local planar patches. Compute bound platforms may restrict the computational duration of the compression algorithm and low-bandwidth platforms can restrict the size of the compression result. The backbone of the approach is an ultra-fast adaptive 3D compression algorithm that transforms swaths of 3D planar surface data into planar patches attributed with image textures. Our approach uses DVO SLAM, a leading algorithm for 3D mapping, and extends it by computationally isolating map integration tasks from local Guidance, Navigation, and Control tasks and includes an addition of a network protocol to share the compressed plane clouds. The joint effect of these contributions allows agents with 3D sensing capabilities to calculate and communicate compressed map information commensurate with their onboard computational resources and communication channel capacities. This opens SLAM mapping to new categories of robotic platforms that may have computational and memory limits that prohibit other SLAM solutions

PlantGL : a Python-based geometric library for 3D plant modelling at different scales

In this paper, we present PlantGL, an open-source graphic toolkit for the creation, simulation and analysis of 3D virtual plants. This C++ geometric library is embedded in the Python language which makes it a powerful user-interactive platform for plant modelling in various biological application domains. PlantGL makes it possible to build and manipulate geometric models of plants or plant parts, ranging from tissues and organs to plant populations. Based on a scene graph augmented with primitives dedicated to plant representation, several methods are provided to create plant architectures from either field measurements or procedural algorithms. Because they reveal particularly useful in plant design and analysis, special attention has been paid to the definition and use of branching system envelopes. Several examples from different modelling applications illustrate how PlantGL can be used to construct, analyse or manipulate geometric models at different scales

Freeform User Interfaces for Graphical Computing

報告番号: 甲15222 ; 学位授与年月日: 2000-03-29 ; 学位の種別: 課程博士 ; 学位の種類: 博士(工学) ; 学位記番号: 博工第4717号 ; 研究科・専攻: 工学系研究科情報工学専

3D Multi-user interactive visualization with a shared large-scale display

When the multiple users interact with a virtual environment on a largescale display there are several issues that need to be addressed to facilitate the interaction. In the thesis, three main topics for collaborative visualization are discussed; display setup, interactive visualization, and visual fatigue. The problems that the author is trying to address in this thesis are how multiple users can interact with a shared large-scale display depending on the display setups and how they can interact with the shared visualization in a way that doesn’t lead to visual fatigue. The first user study (Chapter 3) explores the display setups for multi-user interaction with a shared large-display. The author describes the design of the three main display setups (a shared view, a split screen, and a split screen with navigation information) and a demonstration using these setups. The user study found that the split screen and the split screen with navigation information can improve users’ confidence and reduce frustration level and are more preferred than a shared view. However, a shared view can still provide effective interaction and collaboration and the display setups cannot have a large impact on usability and workload. From the first study, the author employed a shared view for multi-user interactive visualization with a shared large-scale display due to the advantages of the shared view. To improve interactive visualization with a shared view for multiple users, the author designed and conducted the second user study (Chapter 4). A conventional interaction technique, the mean tracking method, was not effective for more than three users. In order to overcome the limitation of the current multi-user interactive visualization techniques, two interactive visualization techniques (the Object Shift Technique and Activity-based Weighted Mean Tracking method) were developed and were evaluated in the second user study. The Object Shift Technique translates the virtual objects in the opposite direction of movement of the Point of View (PoV) and the Activity-based Weighted Mean Tracking method assigns the higher weight to active users in comparison with stationary users to determine the location of the PoV. The results of the user study showed that these techniques can support collaboration, improve interactivity, and provide similar visual discomfort compared to the conventional method. The third study (Chapter 5) describes how to reduce visual fatigue for 3D stereoscopic visualization with a single point of view (PoV). When multiple users interact with 3D stereoscopic VR using multi-user interactive visualization techniques and they are close to the virtual objects, they can perceive 3D visual fatigue from the large disparity. To reduce the 3D visual fatigue, an Adaptive Interpupillary Distance (Adaptive IPD) adjustment technique was developed. To evaluate the Adaptive IPD method, the author compared to traditional 3D stereoscopic and the monoscopic visualization techniques. Through the user experiments, the author was able to confirm that the proposed method can reduce visual discomfort, yet maintain compelling depth perception as the result provided the most preferable 3D stereoscopic visualization experience. For these studies, the author developed a software framework and designed a set of experiments (Chapter 6). The framework architecture that contains the three main ideas are described. A demonstration application for multidimensional decision making was developed using the framework. The primary contributions of this thesis include a literature review of multiuser interaction with a shared large-scale display, deeper insights into three display setups for multi-user interaction, development of the Object Shift Techniques, the Activity-based Weighted Mean Tracking method, and the Adaptive Interpupillary Distance Adjustment technique, the evaluation of the three novel interaction techniques, development of a framework for supporting a multi-user interaction with a shared large-scale display and its application to multi-dimensional decision making VR system