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1 From video sequence as a database for the generation of 3D models to video as a tool for architecture communication Matteo Flavio Mancini; Alessia Moscarelli; Erald Mulla Sapienza Università di Roma, Facoltà di Architettura, Dipartimento di storia, disegno e restauro dell Architettura, Rome, Italy 1. Introduction The purpose of this work is to define a methodology for the use of video sequences as a source of metric data to obtain three-dimensional models in a reliable and fast way, as well as a virtual environment for the representation, fruition and communication of the filmed architectures. An important investigation topic will be the analysis of the metric data provided by the use of informatic tools. For this intent verifi cations will be based on two key features: data s accuracy and their precision. In the fi rst case the compliance to the real metric measurements will be checked by comparing our data with those provided by other techniques already verifi ed and consolidated while, in the second case, the scattering of the achieved results will be verified by repeating several times the algorithm. The experiment will be held using software and optical instruments selected in order to obtain a good balance between costs and quality. This methodology makes it possible to work virtually on an urban context or a specific architectural structure. Thanks to many possible simulations, it may be useful in various fi elds of application such as survey and restoration, monitoring, remote fruition, virtual reconstruction and simulation of possible interventions. The fast data acquisition end the easy further processing make this technology easily and effi ciently applied on various cultural heritage such as architecture, archaeology and urban contexts. The output of this workfl ow leads to a better communication with the final user providing enhanced immersive experience and easier comprehension then traditional static images (rendering, 2d elaborations, photo-manipulation etc.) adding an important feature: motion. Therefore it is also intended as a valid tool for users who are not very familiar or specialized in this field. 2. Instrumentation The instruments used during the shoots are: Canon EOS 550D reflex digital camera equipped with a 18.0Mpx CMOS APS-C (22,3x14,9mm) sensor, a widely diffused entry-level camera with a great quality/price ratio; TOKINA AT-X 116PRO DX optical wide-angle zoom lens (11-16mm F2.8), suitable for large-scale video shooting such as those of urban areas with minimal lens distortion easily corrected in the calibration phase; a steadycam assembled by Sturdycam with specifi c weights kit for the camera-optics combination used to stabilize the hand-held shooting in order to guarantee more efficient camera tracking; a Manfrotto tripod with three head movements in order to make stable 360 shots. 3. Methodology The experimented methodology consists of three phases: the first phase is 969
2 the acquisition of the video-sequence through a hand-held video camera with the aid of a steadycam for its stabilization; the second phase is focused on the pre-processing of the footage with video-tracking software that after carried out the tracking by semi-automatic operations, calibration, generation and orientation of the points cloud, will allow to obtain three-dimensional data including the geometry of the object and the real position of the camera; the third step consists of processing the data prior received to identificate the main geometry of the volumes, allowing us to generate the three-dimensional model of the fi lmed architectures Acquisition The acquisition operations were conducted on a study case of urban scale, Piazza Borghese in Rome. There were conducted two sets of different shots: the first one moving the camera with a 360 rotation, placed on a tripod in a barycentric position into the Fig.1 - Location: Piazza Borghese, Rome. Red line: total path; Red and white line: path analyzed. Cameras at frames 1, 500, 1000, 1500, 2000 are located on the map. Above we have images sequence at frames 1,500,1000, 1500,
3 square. The second set was carried out with hand-held steadycam shooting, walking along the perimeter of the square and pointing always the opposite side (fig.1). In both cases it was kept a fixed focal length of 11mm (equivalent of 17.6mm in a full-frame 35mm format). Shooting was made in FullHD video resolution (1920x1080px,30fps) Video-tracking and point cloud It is called the process that, given a video stream, locates and follows one or more moving objects in the scene. At first, the initial recording is analyzed by a camera tracking algorithm, the software attempts to find common points (groups of pixels) between adjacent frames of a shooting sequence and tracks them for the entire duration of the shot. The purpose of the tracking algorithm is to analyze video frames in order to estimate the extrinsic parameters of the camera: translation, rotation therefore orientation. The intrinsic parameters, such as the internal geometry of the camera, in our case are known in advance (focal distance, lens distortion etc) Video-tracking workflow The process leading from the frame sequence to the three-dimensional point cloud is divided into four steps: camera tracking, camera solving, point cloud 3d projection, the determination (based on one or more known measures) of the scale and orientation of the scene. Once the plate has been imported inside the software and lens distortions are corrected, we perform the camera tracking indicating the number of points and the type of their distribution to be analyzed in each frame. The result at this time is still a two-dimensional data. Through the analysis of the movement of the 2D-markers, we proceed to camera solving, which allows us to identify the internal and external parameters of the camera and rebuilt its movement inside the scene. Thanks to the 2D-markers, camera characteristics and its movement simulation, we can make a three-dimensional projection of markers and the points generated through automatic triangulations. At this time we can perform the refine process, correcting or eliminating points affected by high errors. In this case error represents the observed deviation from the camera point of view between the 2D image of a point and its three-dimensional projection. The last step consist in orienting and scaling the scene according to one or more of the measures taken in the fi eld during the shots (Figg.2-3) Scene reconstruction Once obtained the track point cloud, it is possible to reconstruct the virtual scene in two ways: by a process of image-building modeling, within the same tracking software, or by importing the point cloud into other software that perform meshing operations on the model or can add geometries Video insertion / Video communication We can import the scene and the 3D camera inside a software (cinema 4D, 3dsMax) which allows us to create an animation that includes the project intervention for which we want to simulate the impact. This animation, overlaid 971
4 Fig.2 - Tracking workfl ow: frome plate input to 3d volumes (from left to right). a. Rotoscoping and mask 972 individuation; b. Lens correction; c. Tracking features 2d; 4.Camera solver. d. Camera and scene orientation; e. Image-based Modeling
5 Fig. 3. Overlapping of point cloud with image sequence at frame 500 on the shots taken in the fi eld using matchmoving techniques, allows to get a perfect synchrony between frames and movement (Fig.4). 4. Verifying metric data In order to verify the accuracy of the metric data achieved with this technique, testing the possibility to assess an eventual use for a scientific survey of the cultural heritage, it was decided to compare the results with those obtained on the same subject but with two different and more consolidated techniques: a reality-based like laser-scanning and another image-based like digital photogrammetry. Chosen the LiDAR mesh as reference data and set a range of values between +/- 1 mt, the results are: the photogrammetric point cloud returns a 19,7 cm mean distance and a 23 cm standard deviation; the track point cloud returns a 28,35 cm mean distance and a 59 cm standard deviation. 5. Conclusions It has been tested that a tracking done on a video sequence with not corrected lens distortion, produces a point cloud with a curved shape where it was supposed to represent a building with a clear linear facade. Another consideration is related to the movement of the camera on a tripod to simulate the movement of the laser scanner. The camera is blocked and the only allowed movement is around the vertical axis for 360. The tracking operation does not return an acceptable result as it is not satisfied the parallax principle, which is essential for triangulation process. By performing experiments on the second shooting mode, hand-held camera, it have been identifi ed a number of problems due to the excessive length of 973
6 Fig.4 - Match-moving example: video communication; Fig.5 - Left: LiDAR mesh and photogrammetric point cloud; Right: LiDAR mesh and track point cloud the movie for a single processing: excessive calculation times; need for powerful hardware; high error margins (camera-track e camera-solve); less control and management of the filmed scene: appearance / disappearance of people, presence at the scene of moving objects, reflecting objects, moving clouds in the sky etc. The solution is to divide the sequence in multiple frame-range in order to optimize the working procedure. Once we get the different camera-solving, what follows is a simple operation of the different cameras collimation in order to get the full path of the original camera. 974
7 5.1. Metric considerations Both confrontation results are affected by an error component due to a manual operations of point clouds orienting and scaling based on the LiDAR mesh. A more accurate data editing and a topographic integration with the aid of targets (auto-track features) will strongly reduce this error. If the difference between the mean distances is, in both cases, acceptable for an urban scale volumetric reconstruction, it is not acceptable for the standard deviation that underlines bigger track point cloud dispersion and smaller homogeneity for the track point cloud (Fig.5). References Faugeras O., 1993, Three-Dimensional Computer Vision: a Geometric Viewpoint, MIT Press. Brinkman R., 1999, The Art and Science of Digital Compositing, Morgan Kaufmann, University of California. Gibson S., Hubbold R.J., Cook J., Howard T.L.J., 2003, Interactive reconstruction of virtual environments from video sequences, «Computers & Graphics», 27. Hartley R., Zisserman A., 2003, Multiple View Geometry in Computer Vision, Cambridge University Press. Hartley R., Zisserman A., 2004, Multiple View Geometry in Computer Vision, Cambridge 2nd ed, April, University Pr. Faugeras O., Luong Q.T., 2004, The Geometry of Multiple Images, MIT Press. Hornung E., 2010, The Art and Technique of Matchmoving: Solutions for the VFX Artist, Focal Press/Elsevier. Okun J.A., Zwerman S., 2010, The VES Handbook of Visual Effects: Industry Standard VFX Practices and Procedures, Focal Press/Elsevier. Maggio E., Cavallaro A., 2011, Video Tracking: Theory and Practice, John Wiley & Sons. De Luca L., De Domenico F., 2011, La fotomodellazione architettonica. Rilievo, modellazione, rappresentazione di edifi ci a partire da fotografi e, Dario Flaccovio Editore. Dobbert T., 2012, Matchmoving: The Invisible Art of Camera Tracking, John Wiley & Sons, 2nd ed., November. Hornung E., 2013, The Art and Technique of Matchmoving: Solutions for the VFX Artist, CRC Press. 975
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