Mixed reality for mobile construction site visualization and communication

Tags: visualization, Google Earth, construction site, implementation, building model, 4DStudio, view, Augmented Reality, Woodward C., viewing direction, Hakkarainen M., virtual camera, mobile devices, BIM, client-server solution, MapStudio, model data, application, models, applications, construction applications, building construction, construction sector, mobile application, Virtual Reality, Research Professor Mika Hakkarainen, Mixed Reality, Charles Woodward, placemarks, Research Scientist, visualization model, Kantonen T., REFERENCES Aittala M., Tekla Structures, The University of Michigan, Rainio K., mobile augmented reality systems, Mobile Mixed Reality, VTT, geographic coordinates, Geographical coordinates, block model, the view, building models, reality visualization, system architecture, visual information, The client
Content: 10th International Conference on Construction Applications of Virtual Reality, 2010 MIXED REALITY FOR MOBILE CONSTRUCTION SITE VISUALIZATION AND COMMUNICATION Charles Woodward, Research Professor Mika Hakkarainen, Research Scientist Otto Korkalo, Research Scientist Tuomas Kantonen, Research Scientist Miika Aittala, Research Scientist Kari Rainio, Senior Research Scientist Kalle Kдhkцnen, Chief Research Scientist VTT Technical Research Centre of Finland email: [email protected], http://www.vtt.fi/multimedia ABSTRACT: This article gives an presentation of the AR4BC (Augmented Reality for Building and Construction) software system, consisting of the following modules. The 4DStudio module is used to read in BIMs and link them to a project time table. It is also used to view photos and other information attached to the model from mobile devices. The MapStudio module is used to position the building model on a map using geographic coordinates from arbitrary map formats, e.g. Google Earth or more accurate ones. The OnSitePlayer module is the mobile application used to visualize the model data on top of the real world view using augmented reality. It also provides the ability to insert annotations on the virtual model. The OnSitePlayer may be operated either stand-alone, or in the case of large models, as a client-server solution. The system is compatible with laptop PCs, hand held PCs and even scales down to mobile phones. Data glasses provide another display option, with a novel user interface provided by a Wiimote controller. A technical discussion on methods for feature based tracking and tracking initialization is also presented. The proposed system can also be used for pre-construction architectural AR visualization, where in-house developed methods are employed to achieve photorealistic rendering quality. KEYWORDS: BIM, 4D, augmented reality, mixed reality, tracking, rendering, mobile devices, client-server. 1. INTRODUCTION The real estate and building construction sector is widely recognized as one of the most promising application fields for Augmented Reality (AR). Recent developments in mobile processing devices, camera quality, sensors, wireless infrastructure and tracking technology enable the implementation of AR applications in the demanding mobile environment. The AR technologies and applications seem to be converging towards more mobile solutions that can almost be characterized as commodities. This is particularly welcomed by the real estate and construction sector where the implementations and benefit realisation of new technologies have been often hindered by cost. The overall convergence towards simpler and more accessible solutions has been more generally identified as a development trend in (Marasini et al. 2006). The following presents some main use-cases of applying mobile Augmented Reality in the real estate and building construction sector: 1. Visualization and verification of tasks and plans during construction work. At some advanced construction sites, 3D/4D Building Information Models (BIMs) are starting to replace paper drawings as reference media for construction workers. However, the model data is mostly hosted on desktop systems in the site office, far away from the target situation. Combined with Augmented Reality, 4D BIMs could facilitate on-the-spot comparisons of the situation at the construction site with the building's planned state and properties at any given moment. 2. Interactive presentations of newly designed solutions in the context of existing artifacts. This particularly covers the modernization of existing facilities and building renovations. Such operations are responsible for a 1
Nov. 4-5, 2010 continuously growing share of the building construction business in many countries. Therefore solutions for visualizing new designs over currently existing environments are also of importance. 3. Interactive presentations of temporary objects and their placing. A construction site is a continuously changing working environment where temporary Work Arrangements are commonplace. One key application is to produce realistic visualizations and simulations for site personnel to improve site safety and productivity. 4. Characteristics of the Built Environment solutions during their life-cycle presented with the actual building object. BIM information used for visualizing the building elements during construction could often serve also for the building's life-cycle applications. Altogether, mobile augmented information available at the construction site would have various applications in construction Work Planning, verification, training and safety, life-cycle management as well as for communication and marketing prior to construction work. The related camera tracking technologies open up further application scenarios, enabling the implementation of mobile location-based visual feedback from the construction site to the CAD and BIM systems. The tracking and interaction techniques can thus be made to serve the complete spectrum of Mixed Reality (Milgram and Kishino 1994), forming a seamless interaction cycle between the real world (augmented with virtual 3D/4D model data) and digital building information (augmented with real world data). The goal of the Finnish national research project AR4BC (Augmented Reality for Building and Construction) is to bring the full potential of 4D BIM content and visualization directly to the construction site via mobile augmented and mixed reality solutions. This article gives an overall presentation of the AR4BC software system, its current state and future plans. The article is organized as follows. Section 2 gives a brief discussion of earlier work in this research field. Section 3 gives an overview of the software architecture. Section 4 explains the implementation and functionality of the core software modules in several subchapters accordingly. Our client-server solution is presented in Section 5. The camera tracking solutions are discussed in Section 6, followed with methods for photorealistic rendering in Section 7. Items for future work are pointed out in Section 8 and concluding remarks are given in Section 9. 2. RELATED WORK The use of Augmented Reality for building and construction applications has been studied by various authors over the years. The first mobile system for general AR applications was presented by Feiner et al. (1997). Extensive research on outdoor AR technology for architectural visualization was carried out a decade ago by Klinker et al (2001). Reitmayr and Drummond (2006) were among the first to present robust feature based and hybrid tracking solutions for outdoor mobile AR. Izkara et al. (2007) provide a general overview of today's mobile AR technology, with specialized applications in safety evaluation. The thesis by Behzadan (2008) gives a wide discussion of AR application in construction site visualizations. Schall et al. (2008) describe a mobile AR system for visualizing underground infrastructure and extend it to state-of-the-art sensor fusion for outdoors tracking in (Schall et al. 2009). These general leads provide a wealth of further references to other related work in the field. Among our own work, (Woodward et al. 2008) gives a discussion of our general AR related work in the building and construction sector, including mobile, desktop and web based augmented reality solutions. Our work on mobile AR applications dates back to the client-server implementation on PDA devices (Pasman and Woodward 2004; VTT video 2003). Our next generation implementation, "Google Earth on Earth" (Honkamaa et al. 2007; VTT video 2005), provided a marker-free solution for architectural visualization by combining the building's location in Google Earth, the user's GPS position, optical flow tracking and user interaction for tracking initialization. Our mobile AR solution was generalized in (Hakkarainen et al. 2009) to handle arbitrary OSG formats and IFC (instead of just Google Earth's Collada), 4D models for construction time visualization (instead of just 3D), and mobile feedback from the construction site to the design system ("augmented virtuality"). In this article we present the most recent developments and implementation issues with the AR4BC system. These include: managing different map representations (e.g. GE, GDAL); overcoming situations where technology fails to produce automatic solutions (e.g. GPS and map accuracy, tracking initialization and reliability); other mobile interaction issues (e.g. operation with HMD); client-server solutions to handle arbitrarily large models on mobile devices with real time performance; and general discussions of outdoors tracking and photorealistic rendering methods. It is expected that the above issues, regarding practical implementation of two-way mobile mixed reality interaction with complex 4D BIM models, will be of help to other researchers working in the field as well. 2
10th International Conference on Construction Applications of Virtual Reality, 2010 3. SYSTEM OVERVIEW The AR4BC system architecture presented in (Hakkarainen et al. 2009) has been revised and is shown as a schematic illustration in Figure 1. Fig. 1: System overview. The 4DStudio module is responsible for handling and modifying the 4th dimension, i.e. the timing information of the BIM. It allows the user to define timelines for the construction steps, as well as visualize the workflow for given time ranges. 4DStudio also provides the user interface to browse and visualize incoming reports created by the mobile user with OnSitePlayer. The MapStudio module is used to position the BIMs on a map using geographic coordinates (position and orientation). It takes as input a map captured from Google Earth or other map data bases. MapStudio can also be used to add some additional models around the construction site, to mask the main construction model or to add visual information of the surroundings. The OnSitePlayer module provides the user interface for augmented reality visualization, interaction and capturing feedback from the construction site. OnSitePlayer is able to augment the models in the right position (location, orientation and perspective) in the real world view by utilizing the model's geographic coordinates in combination with the user's position. User positioning can be automatically computed using GPS, or manually defined using a ground plan or a map of the site. The OnSiteClient module provides a lightweight mobile version of OnSitePlayer. Rendering of complex models is accomplished by using 2D spherical mappings of the model projected to the client's viewing coordinates. The viewing projection is provided by the server module, OnSiteServer, which only needs to be updated once in a while (not in real-time). The goal is that the user should not even be aware of communicating with a server, and have the same user experience as with the stand-alone OnSitePlayer. Our ALVAR software library provides generic implementations of marker-based and markerless tracking. For rendering, OpenSceneGraph 2.8.2 is used and the GUI is built using the wxWidgets 2.8.10 framework. The applications can handle all OSG supported file formats via OSG's plug-in interface (e.g. OSG's internal format, 3DS, VRML) as well as IFC, using the parser module developed by VTT. 4. IMPLEMENTATION This Section presents the basic implementation of the system. Emphasis is put on recenlty implemented functionality. The discussion is given mainly from the user interface point of view, omitting most technical and mathematical details. The client-server solution, as well as tracking and rendering issues, are treated in three separate Sections, with a discussion reaching also beyond the current implementation. 3
Nov. 4-5, 2010 4.1 4DStudio The 4DStudio application takes the building model (in IFC or some other format) and the construction project schedule (in MS Project XML format) as input. 4DStudio can then be used to link these into a 4D BIM. To facilitate the linking, the application uses project hierarchies. This means that a large number of building parts can be linked together at once. Once the linking has been performed, 4DStudio outputs project description files including the model itself and the timing information as an XML file. A recently implemented feature is the ability to read in 4D BIMs defined with Tekla Structures. Version 16 of Tekla Structures contains a new module called Task Manager, which can be used to define the building tasks and link them with building parts. The model data loaded with Tekla Structures is exposed through the Tekla Open API. Using this API, the tasks and links defined in Tekla Structures Task Manager can be written to an XML file. 4D Studio can then read this XML file and import the building tasks and links. Various tools are provided for visualization and interaction with the 4D BIMs; see (Hakkarainen et al. 2009). 4DStudio has a list of all the building parts, a list of all building project tasks, as well as a list of all linked tasks, from which the user can select desired elements for visualization. Also, feedback report items are shown in a list. The report items describe tasks or problems that have been observed at the construction site by workers. Each item contains a title, a task description, a time and location of the task, and optionally one or several digital photos. The user can easily move from the report items list to the spatial 3D model and back. 4.2 MapStudio The MapStudio application is used to position the models into a geographical coordinate system, using an imported map image of the construction site. Geographical coordinates denote the combination of GPS position and rotation around the model's vertical axis. The models are imported from 4DStudio, and can be of any OSG compatible format or IFC format. The model can either be a main model or a so-called block model. A block model has an existing counterpart in the real world, usually a building or more generally any object in the real world. Block models can be used to enrich the AR view, or these models are used to mask the main model with existing buildings, in order to create the illusion that real objects occlude the virtual model. If needed, the system can also add clipping information to the models, for example the basement can be hidden in on-site visualization. As one option, the geographical map can be imported from Google Earth. First, MapStudio initializes the Google Earth application. Second, the user locates the desired map position in Google Earth's view and selects the viewing range to allow for the addition of building models, user position and other information (see below) on the map. However, Google Earth maps are not very accurate and can contain errors in the order of tens of meters. Therefore, MapStudio has recently been enhanced with the option to use raster geospatial data formats like GeoTiff. The image import is done using the open source Geospatial Abstraction library. GDAL supports several geospatial raster formats, which typically offer much better accuracy than Google Earth maps. After the required view is defined in Google Earth or another map source, the user switches back to MapStudio. MapStudio captures the chosen view of the map and gathers the view's GPS values via Google Earth COM API or GDAL. Now the map layout is available in MapStudio and the GPS information is transferred to the rendering coordinates and vice versa. The user can finally position the BIM models on the map, either by entering numerical parameters manually or by interactively positioning the model with the mouse. See Figure 2. Fig. 2: Building model placed in geo coordinates using MapStudio. 4
10th International Conference on Construction Applications of Virtual Reality, 2010 Once the main model has been defined, additional block models and other information may be added and positioned in geographical coordinates. Finally, the AR scene information is stored as an XML based scene description, the AR4BC project file, ready to be taken out for mobile visualization on site. 4.3 OnSitePlayer The OnSitePlayer application is launched at the mobile location by opening a MapStudio scene description, or by importing an AR4BC project file containing additional information. The application then provides two separate views in tabs; a map layout of the site with the models including the user location and viewing direction (see Figure 3) and an augmented view with the models displayed over a real-time video feed (see Figure 4). The user is able to request different types of augmented visualizations for the model based on time. The default settings show the current work status based on the current time and planned work start time. The system uses different colors to display different phases (finished, under construction, or not yet started). The user can also define the visualization start-time and end-time freely (e.g. past work or planned tasks for next week). The model can be clipped on the view by using clipping planes (6 clipping planes, 2 for each axis). The model can also be shown partially transparent to see the real and existing structures behind the virtual ones. The OnSitePlayer is able to store augmented still images and video from the visualization, to be reviewed at the office. The user is also able to create mobile reports, consisting of still images annotated with text comments. Each report is registered in the 3d environment at the user's location, camera direction, and moment in time. The reports are attached to the BIM via XML files and are available for browsing with 4DStudio, as explained above. 4.3.1 Interactive positioning Normally, the user's location on the site is determined using an external GPS device via a Bluetooth connection. However, GPS positioning does not always work reliably (e.g. when indoors) and is also the source of several errors. Therefore, the user has an option to indicate their location interactively. The system presents the user with the same map layout as used in the MapStudio application and an overlayed camera icon representing the location and viewing direction. The latter can be adjusted using simple stylus/touch interactions. See Figure 3. Fig. 3: User position, building model and placemark on the map layout. Interactive positioning is strongly recommended when using Google Earth maps and/or consumer level GPS devices. The GPS accuracy is roughly 10-50 meters, and if there is also an error in the map's geographical information the total error is even more significant. By using manual positioning, the errors in model and user-location positioning are aligned and thus eliminated from the model orientation calculation. 4.3.2 Aligning options Once the position of the user and the virtual model are known, they provide the proper scale and perspective transformations for augmented reality rendering. Combined with compass information, model based tracking initialization, feature based tracking and hybrid methods, the system should now be able to automatically augment the model over the live video feed of the mobile device. In practice however, some part of the tracking pipeline may always fail or be too inaccurate; thus we provide also interactive means for initial model alignment before tracking methods take over. The aligning of the video and the models can be achieved in several ways. 5
Nov. 4-5, 2010 Once the scene is loaded and the user's position is initialized, the models are rendered as they look from the user perspective, although not at right location yet. The user can then interactively place the model in the right location in the view. This is a reasonably easy task if the user has some indication of how the building model should be placed in the real world. For example, during construction work the already installed building elements can be used as reference. Placing an architect's visualization model on the empty lot can be more challenging. Therefore, two additional interactive positioning options are provided; block models and placemarks. Block models, as described above, are 3D versions of existing features (like buildings) in the real world. By placing the virtual block model on top of the real world counterpart, the user is able to intuitively align the video view and models. However, this approach requires modeling parts of the surrounding environment which might not always be possible or feasible. A more general concept is to use the placemarks and viewfinder approach. In MapStudio, the author is able to place placemarks on the view (see Figure 3). The placemarks only have GPS coordinates as properties. The placemarks should mark clearly visible and observable elements in the real world (e.g. street corner, chimney, tower, etc.). With OnSitePlayer, the user then selects any of the defined placemarks and points the viewfinder towards it. When the user presses a key to lock the placemark with the viewfinder, the system calculates the "compass" direction accordingly and aligns the actual models to the view. Afterwards the models are kept in place by tracking methods. See Figure 4. Fig. 4: Mobile video showing viewfinder for the placemark, and building model augmented with OnSitePlayer. 4.3.3 HMD and Wiimote In bright weather conditions, the laptop's or hand held device's screen brightness might not be sufficient for meaningful AR visualization. Head mounted displays (HMD) offer a better viewing experience, but they also create issues for user interaction. When aligning the model and video over a HMD, the user should not have to operate a mouse/stylus or find the right keys from the keyboard to press. To overcome this problem, a Nintendo Wiimote is integrated into the system and used as an input device. With a Wiimote, the user is able to select between placemark and model based aligning, as well as lock the model to the view. The user is also able to use Wiimote to change the viewing direction (compass) and altitude value to meet the real world conditions. 5. CLIENT-SERVER SOLUTION Virtual building models are often too complex and large to be rendered with mobile devices at a reasonable frame rate. This problem is overcome with the development of a client-server extension for the OnSitePlayer application. Instead of trying to optimize the 3D content to meet the client's limitations, we developed a 2D image based visualization technique that replaces the 3D model with a spherical view of the virtual scene surrounding the user (Hakkarainen and Kantonen 2010). OnSiteClient is used at the construction site while OnSiteServer is running at some remote location. The client and server share the same scene description as well as the same construction site geographical information. The client is responsible for tracking the user's position, while the server provides projective views of the building model to the client. The projective views are produced for a complete sphere surrounding the user's current position, and the client system chooses the correct portion of the sphere to be augmented by real time camera tracking. Our solution generally assumes that the user does not move about while viewing. This is quite a natural assumption, as viewing and interacting with a mobile device while walking would be quite awkward or even dangerous, 6
10th International Conference on Construction Applications of Virtual Reality, 2010 especially on a construction site. The user's location in the real world is transmitted to the server, and augmenting information is updated by the user's request. Data communication between the client and server can be achieved using either WLAN or 3G. The communication delays from a fraction of a second to a couple of seconds are not an issue, since the projective views used for real time augmenting only need to be updated occasionally. After receiving the user's position, the server renders the scene for a whole sphere surrounding the corresponding virtual camera position. In the implementation, the sphere is approximated by triangles, an icosahedron in our case. An icosahedron was chosen since it is a regular polyhedron formed from equilateral triangles, therefore simplifying the texture generation process. The number of faces is also an important parameter as increasing number of faces increases the resolution of the resulting visualization and at the same time increases the number of images to be transferred to the client. To create the triangle textures required for the spherical view around the user's position, the server aligns the virtual camera so that each triangle is directly in front of the camera, perpendicular to the viewing direction, and with one of the triangle edges aligned horizontally. The view of the scene is then rendered and the resulting image is capture and stored for use as a triangle texture in the client side rendering. For each image, the depth information of the picture is analyzed at the pixel level to verify whether there is 3D model information present or not. If there is no model information present, the pixel's alpha value is set fully transparent to allow the client's video feed to be visible. If the entire image does not contain any 3D model information, it is considered as fully transparent. After the spherical 2D images are created, they are sent to the client. Since the client has the same faceted sphere representation as the server, the client is able to create a 3D illusion of the scene from the user's view point by positioning the sphere at the virtual camera and rendering the textured sphere over the video image. After aligning the view with the current viewing direction, the user is able to use camera tracking to keep the 2D visualization in place and pan/tilt the view in any direction as desired. The same sphere visualization can be used as long as the user remains in the same place. 6. TRACKING Camera tracking is one of the most important tasks in video-see-trough augmented reality. In order to render the virtual objects with the correct position and orientation, the camera parameters must be estimated relative to the scene in real-time. Typically, the intrinsic parameters of the camera (e.g. focal length, optical center, lens parameters, etc.) are assumed to be known, and the goal is to estimate the exterior parameters (camera position and orientation) during tracking. The combination of several sensors, from GPS to accelerometers and gyros, can be used to solve the problem, but computer vision based approaches are attractive since they are accurate and do not require extra hardware. Markerless tracking methods refer to a set of techniques where the camera pose is estimated without predefined markers using more advanced computer vision algorithms. A comprehensive review of such methods can be found in (Lepetit 2005). We developed two markerless tracking methods to be used in different use cases. With the OnSiteClient application, the user is assumed to stand at one position, relatively far from the virtual model, and explore the world by panning the mobile device (camera). Thus, the pose estimation and tracking problem is reduced to estimating the orientation only. On the other hand, with the stand-alone OnSitePlayer, we allow the user to move around the construction site freely, which requires both the position and orientation of the camera to be known. In both cases, we assume that the scene remains mostly static. In the first option, we use GPS to obtain the initial position, and the orientation is set interactively with the help of a digital compass. As the user starts to observe the virtual model and rotate the camera, we detect strong corners (features) from the video frames and start to track them in the image domain using the Lucas & Kanade algorithm as implemented in the OpenCV computer vision library. Every image feature is associated with a 3D line starting from the camera center and intersecting the image plane at the feature point. As the tracker is initialized, the pose of the camera is stored and the update process determines the changes in the three orientation angles by minimizing the re-projection errors of the 3D lines relative to image measurements. We use a Levenberg-Marquardt (LM) optimization routine to find the rotation parameters that minimize the cost. However, moving objects such as cars or people and drastic changes in illumination like direct sunlight or reflections can cause outliers to become tracked features. Hence, we apply a robust Tukey M-estimator in the optimization routine. After each iteration loop, the outliers are removed from the list of tracked features by examining the re-projection errors of each feature, and deleting the ones with a re-projection error larger than a pre-defined limit. New features are added to the list when the camera is rotated outside the original view, and features that flow outside the video frame are deleted. 7
Nov. 4-5, 2010 In the second option, we use the actual 3D coordinates of the tracked features for updating the camera pose. We obtain them by first initializing the camera pose and rendering the depth map of the object using OpenGL. Then, we detect strong corners from the video, and calculate their 3D coordinates using the depth map. The features that do not have a depth are considered as outliers and removed from the list. The tracking loop proceeds similarly to the first case, except that all of the six exterior parameters of the camera are estimated. The initial position of the camera is acquired interactively by defining six or more point pairs between the 3D model and the corresponding image of the model within the video frame. As the point correspondences are defined, the direct linear transformation (DLT) algorithm is used to find the pose which is then refined using LM. Fig. 5: OnSitePlayer using UMPC, and underlying 3D features for tracking. Although the presented methods allow us to visualize different stages of the construction on-site, they suffer from the same problems: drift and lack of automated initialization and recovery. Since the features are tracked in the image domain recursively, they eventually drift. Additionally, the pose estimation routines are recursive, and they proceed despite drifting features and severe imaging conditions such as a badly shaking camera. When the tracking is lost, the user must re-initialize it manually. To overcome these problems, we have two solutions planned. First, we will take advantage of feature descriptors like SURF and Ferns, and store key poses along with corresponding features and descriptors. The initialization and recovery could be then performed by detecting features from the incoming video frames and pairing them with the stored ones to find the closest key pose. Second, we will use the initial pose from the digital compass and GPS as a-priori, and render the model from the corresponding view. Then, the rendered model could be compared to the video frame to fine-tune the camera pose parameters. We will also study the possibility of applying these approaches to detect and compensate for drift. 7. RENDERING Photorealistic real-time rendering techniques are important for producing convincing on-site visualization of architectural models, impressive presentations to different interest groups during the planning stage and accurate building renovation mock-ups. We have experimented with some rendering and light source discovery methods described in (Aittala 2010) and integrated them into the OnSitePlayer application. However, on-site visualization of architectural models differs somewhat from general purpose rendering. For optimal results, the methods should be adapted to the particular characteristics of the application. In the following, we present some key characteristics that are typical for on-site visualization applications. Typical architectural models originate from 3D CAD programs, which are often geared towards design rather than photorealistic visualization. The data is often stored in problematic format from rendering standpoint. Polygons may be tessellated very unevenly. For example, a large exterior wall may be represented by a single polygon, whereas the interiors may contain highly detailed furniture (of course, invisible from outside). Elongated polygons and flickering coplanar surfaces are also common. Hence, methods which are insensitive to geometric quality are prefered; for example shadow maps to shadow volumes. The lighting conditions can be complex due to a wide variance of skylight and sunlight in different weather conditions and throughout the day. Regardless, the illumination can be characterized as a linear combination of very smooth indirect skylight and intense, directional and shadow casting sunlight. In terms of rendering, the former provides hints for ambient occlusion methods and the latter for soft shadows. Combining these two components in different colors, intensities, sunlight direction and shadow blurriness can produce a wide array of realistic appearances. 8
10th International Conference on Construction Applications of Virtual Reality, 2010 We have experimented with a screen-space ambient occlusion method and a soft shadow map algorithm as described in (Aittala 2010). However, it appears that high frequency geometric details found in most architectural models cause severe aliasing artifacts for the screen-space ambient occlusion method. The aliasing can be reduced by supersampling (rendering to a larger buffer), but this comes with an unacceptable performance penalty, especially for mobile devices. We are planning modifications to the algorithm based on the observation that the model and the viewpoint are static. Coarse illumination could be computed incrementally at high resolution and averaged across frames. Likewise, the present shadowing method occasionally has problems with the high depth complexity of many architectural models. We plan to experiment with some alternative shadow mapping methods. Determining the lighting conditions can be simplified and improved by considering the lighting as a combination of ambient light (the sky) and a single directional light (the sun). The direction of the sunlight is computed based on the time of day, the current orientation and GPS information. What remains to be determined are the intensity and the color of both components, as well as the shadow sharpness. Our lightweight and easy to use inverse rendering method (Aittala 2010) is based on a calibration object, such as a ping pong ball. We plan to adapt it to the particular two-light basis of this problem. More complex methods would make use of physical skylight models and attempt to recover the model parameters. Changing the lighting also presents a challenge; while the sun direction and the sky color themselves change little over reasonable periods of time, drifting clouds may block the sun and cause notable illumination changes during a single viewing session. Finally, the real world camera aberrations are simulated in order to produce a more convincing result when embedding the rendered images over the video. The quality of rendered images is often unrealistically perfect, whereas the actual cameras exhibit a wide range of imperfections. This makes the rendered models stick out and look artificial over the video, even if the rendering itself is realistic. Effects such as unsharpness, noise, Bayer interpolation, in-camera sharpening and compression are simulated as post-processing effects (Klein and Murray 2008). Other considerations related to images, are with the use of properly gamma corrected color values at all stages. For example, linearized values are used up until the final display. Also multisample antialiasing is used, which is particularly helpful with typical high-frequency geometric detail such as windows. 8. FUTURE WORK Up to now, we have conducted field trials mainly with architectural visualization, for example, the new Skanska offices in Helsinki (Figures 2-4; VTT video 2009). Experiments including 4D BIMs have been conducted with models "taken out of place", without connection to the actual construction site. Field trials at a real construction sites are planned to start at end of year 2010. In the pilot test cases, we look forward to not only demonstrating the technical validity of the system, but also obtaining user feedback to be taken into account in future development. Our goal is to complete the AR4BC system with the intended core functionality in the near future. Plans for tracking and rendering enhancements were discussed in the two previous Sections. For example, in the AR visualization of a new component in the Forchem chemical plant (Figure 5; VTT video 2010), feature based tracking was not yet fully integrated with the AR4BC geographic coordinate system, and tracking initialization was done manually. Photorealistic rendering is another area where a lot of the required technology already exists, but there is room for improvements for long time to come. Since the OnSiteClient software is free of all heavy computation (3D tracking, rendering, etc.) it could actually be ported also to very lightweight mobile devices. Current smart phones and internet tablets offer GPS, WLAN and other connectivity, compass, accelerometer, large and bright screens, touch interaction and other properties all at affordable prices. This makes them a perfect choice for future mobile AR applications, both for professional use as well as for wider audiences. 9. CONCLUSION In this article we have given an description of the AR4BC software system for mobile mixed reality interaction with complex 4D BIM models. The System Supports various native and standard CAD/BIM formats, combining them with time schedule information, fixing them to accurate geographic coordinate representations, using augmented reality with feature based tracking to visualize them on site, applying photorealistic rendering for a convincing experience, with various tools for mobile user interaction as well as image based feedback to the office system. The client-server solution is able to handle arbitrarily complex models on mobile devices, with the potential for implementation on lightweight mobile devices such as camera phones. 9
Nov. 4-5, 2010 10. REFERENCES Aittala M. (2010). Inverse lighting and photorealistic rendering for augmented reality, The Visual Computer. Vol 26 No. 6-8, 669-678. Behzadan A.H. (2008). ARVISCOPE: Georeferenced Visualisation of Dynamic Construction Processes in Three-Dimensional Outdoor Augmented Reality, PhD thesis, The University of Michigan. Hakkarainen M. and Kantonen T. (2010), VTT patent application PCT/FI2010/050399. Hakkarainen M., Woodward C. and Rainio K. (2009). Software Architecture for Mobile Mixed Reality and 4D BIM Interaction, in Proc. 25th CIB W78 Conference, Istanbul, Turkey, Oct 2009. Honkamaa P., Siltanen S., Jдppinen J., Woodward C. and Korkalo O. (2007). Interactive outdoor mobile augmentation using markerless tracking and GPS, Proc. VRIC, Laval, France, April 2007, 285-288. Feiner S., MacIntyre B., Hцllerer T., and Webster A. (1997), A touring machine: prototyping 3D mobile augmented reality systems for exploring the urban environment. In Proc. ISWC'97, Cambridge, MA, USA, October 13, 1997. Izkara J. L., Perez J., Basogain X. and Borro D. (2007). Mobile augmented reality, an advanced tool for the construction sector, Proc. 24th CIB W78 Conference, Maribor, Slovakia, June 2007, 453-460. Klein G. and Murray D. (2008). Compositing for small cameras. Proc. The ISMAR 2008, Cambridge, UK, September 2008, 57-60. Klinker G., Stricker D. and Reiners D. (2001). Augmented reality for exterior construction applications, in Augmented Reality and Wearable Computers, Barfield W. and Claudell T. (eds.), Lawrence Elrbaum Press 2001. Lepetit V and Fua P. (2005). Monocular model-based 3D tracking of rigid objects, Found. Trends. Comput. Graph. Vis., Vol. 1, No. 1, Now Publishers Inc , Hanover, MA, USA 1-89. Marasini, R., Dean, J. and Dawood, N. (2006). VR- roadmap: A vision for 2030 in built environment, Centre for Construction Innovation and Research, School of Science and Technology, University of Teesside, UK. Milgram P. and Kishino F. (1994). A taxonomy of mixed reality visual displays, IEICE Transactions on Information Systems, Vol E77-D, No 12, December 1994. Pasman W. and Woodward C. (2003). Implementation of an augmented reality system on a PDA, Proc. ISMAR 2003, Tokyo, Japan, October 2003, 276-277. Reitmayr G. and Drummond T. (2006) Going out: robust, model based tracking for outdoor augmented reality, Proc. ISMAR2006, Santa Barbara, USA, 22-25 October 2006, 109-118. Schall G., Mendez E. and Schmalstieg D. (2008) Virtual redlining for civil engineering in real environments, Proc. ISMAR 2008, Cambridge, UK, September 2008, 95-98. Schall G., Wagner D., Reitmayr G., Taichmann E., Wieser M., Schmalstieg D. and Hoffmann-Wellenhof B. (2009), Global pose estimation using multi-sensor fusion for outdoors augmented reality, Proc. ISMAR 2008, Orlando, Florida, USA, October 2009, 153-162. Woodward C., Lahti J., Rцnkkц J., Honkamaa P., Hakkarainen M., Jдppinen. J., Rainio K., Siltanen S. and Hyvдkkд J.. Virtual and augmented reality in the Digitalo building project, International Journal of Design Sciences and Technology, Vol 14, No 1, Jan 2007, 23-40. VTT video (2003). http://virtual.vtt.fi/virtual/proj2/multimedia/mvq35p/arpda.html. VTT video (2005). http://virtual.vtt.fi/virtual/proj2/multimedia/movies/aronsite-poyry-ge2.wmv. VTT video (2009). http://virtual.vtt.fi/virtual/proj2/multimedia/movies/ar4bc640x480.wmv. VTT video (2010). http://virtual.vtt.fi/virtual/proj2/multimedia/movies/forchem_augmentation.avi. 10

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