Interactive 4D Overview and Detail Visualization in Augmented Reality

Interactive 4D Overview and Detail Visualization in Augmented Reality

Figure 1: Cluttered visualization of time-oriented data. By simply blending a single or multiple points in time into the real world view the

visualization either becomes difficult to understand or changes between different points in time become difficult to track. Left and Middle:

Visualization of two consecutive points in time in separated views. Clutter and complex changes make it hard to comprehend the changes.

Right: Visualizing two different points in time in one view with different shadings leads to image clutter as well.

vide efficient and effective analysis of the data. Existing techniques

range from very simple line plots [9] and bar charts [9] up to visualization

suites [17] which allow combinations of visualization techniques

in an sophisticated way (see [1] for an extensive review on

visualization techniques of time dependent data). However, while

existing visualization techniques for time-oriented data rather have

been designed to study the data in front of a PC, we aim at an onsite

data visualization. In addition to traditional visualization techniques

for time dependent data, we provide the user with contextual

information about the real world relation of the data he studies.

As-Planned versus As-Build Providing this relation in AR

was demonstrated in applications which compare as-planned with

as-built data. An interactive comparison for this kind of data was

demonstrated by Schoenfelder et al. [23]. The approach overlays

a virtual 3D copy of planned structure on top of a real factory

building.While this approach allows to compare the current point in

time with a previous one, the visualizations may suffer from clutter

if many parts differ in the visualization. Therefore, this techniques

may fail to generate comprehensible visualizations for large

amounts changes, such as tasks for progress or completion analysis.

Visualization of progress analysis was discussed for AR by

Golparvar-Fard et al. within the scope of the 4DAR project [7, 6].

Instead of presenting the actual data of each point in time, the system

computes a single value, such as the current level of completion

and display the value by using a color coding of the 3D real world

object. While this approach allows to study multiple differences between

planned data and the current real world situation, it does not

allow for detailed analysis of the data. Since this system is moreover

based on images, which are augmented offline, the system does

not allow to study data on-site.

In this paper, we extend the idea of color coding real world 3D

objects by adding time-oriented overview and object-dependent detail

information into an interactive 4D overview and detail visualization.

By introducing multiple layers of abstraction, we are able

to visualize multiple previous points in time registered to the current

real world. In order to conquer such complex visualizations,

we make use of Focus+Context (F+C) and Overview+Detail techniques

[4] .

Focus+Context Visualizations One of the main strengths of

AR is the ability to display computer generated renderings within

the context the real world environment provides. Focus+Context

(F+C) techniques can effectively communicate the combination of

renderings and the real world context. For visual discrimination

of focus and context objects, F+C techniques apply for instance

distortion techniques or in-place visual encodings through stylizations

of relevant structures. In-place F+C encodings do not modify

the structure itself, instead they try to emphasize focus elements

by adding visual communicators, such as frames, by manipulating

attentive properties per pixel, such as saturation, brightness

or hue, or by reducing detail on contextual elements through

non-photorealistic rendering (NPR) abstraction techniques [28].

Kalkofen et al. [11] [12] discuss visual abstraction of context elements

while an automatic modulation of different attentive parameters

in AR was demonstrated by Mendez et al. [18] and Veas et

al. [29]. They show how an automatic modulation of saliency values

is able to direct user’s attention to a chosen focus element within

an arbitrary environment.

A number of researchers have also demonstrated the ability of

spatial distortions to emphasize focus elements on desktop systems,

either generated from 3D CAD [20] or from volumetric data [30].

However, only the work on AR explosion diagrams [13] and recent

work by Sandor et al. [21] demonstrate the ability of spatial deformation

techniques to AR.

Overview and Detail Visualizations While F+C visualizations

combine different classes of data in a single view, overview

and detail visualizations distribute the data either temporal or spatial

over two or more views [4]. Traditional time browsing approaches,

such as presented by Keil et al. [16] allow to temporarily

separate both classes. The systems allow to sequentially browse the

3D object of interest in time, registered in their current real world

environment. Nevertheless, they do not provide the capability to

solve complex analysis tasks over time that require to compare data

of different points in time.

Temporarily separating multiple views comes at the price of

higher cognitive load [25]. In consequence, researchers have shown

the view separation in space. Depending on the content, either 2D

or 3D window configurations may be used. Ball et al. [2] demonstrated

a 2D setup with multiple windows presenting the source

code of an application. In contrast, Keil et al. [16] presented the

idea of a 3D World in Miniature (WIM) [27] presentation rendered

as a small inset often at the bottom of the screen. This idea was

applied to AR by Kalkusch et al. [14] providing an overview during

indoor navigation. In addition, Bell et al. [3] demonstrated the

prospects of a WIM in AR to request textual details of interiors

while providing an overview of the user’s current environment as


While the task of analyzing 3D data over time within AR requires

effective visualization tools we combine overview and detail

with focus and context visualization in AR. This allows us to analyze

time dependent data first in a 3D registered overview visualization,

before studying structural parts of the data in detail using

an F+C visualization. The main goal of this approach is to enable a

user to inspect possible sources of errors by traveling back in time

or to inspect points of slow progress on changing environments like

construction sites.



In order to study 3D data over time, we have to provide visualizations

of multiple versions of a data set representing different points

in time. From psychological literature [25] we know that an analysis

of multiple data sets - one after the other - may suffer from per-

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