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If your main objective is to digitize objects, you should be able to do so on your own by reading the chapter ''Practical Scanning'', which gives a step-by-step recipe to perform a complete object scan and reconstruction. 

If your main objective is to digitise objects, you should be able to do so on your own by reading the chapter ''Practical Scanning'', which gives a step-by-step recipe to perform a complete object scan and reconstruction. 

These are high resolution 9MPx industrial CCD color cameras. While color information is usually not necessary in structured light, it enables us to full color texture the scanned object. In the program code, a white balance is used for the camera, which was chosen ad-hoc to approximately match the color profile of the projector. To capture real true colors, a color calibration would have to be done.

These are high resolution 9MPx industrial CCD colour cameras. While colour information is usually not necessary in structured light, it enables us to full colour texture the scanned object. In the program code, a white balance is used for the camera, which was chosen ad-hoc to approximately match the colour profile of the projector. To capture real true colours, a colour calibration would have to be done.

This is a socalled micro-rotation stage, commonly used in high precision photonic research and production. A larger diameter plate was attached. The rotation stage has a stepper motor which drives a worm-gear. This gives high precision and very high repeatability. Note that the rotation stage does not have an optical encoder. It is reset to 0 degrees at each program start in software. The motor controller can be configured for different levels of microstepping and motor current. Higher motor current provides more torque and less risk of missing steps. Load on the plate should not exceed 20 kg, and be centered around the rotation axis. Objects can be stabilized on the plate using e.g. modeling clay.

This is a so-called micro-rotation stage, commonly used in high precision photonic research and production. A larger diameter plate was attached. The rotation stage has a stepper motor which drives a worm-gear. This gives high precision and very high repeatability. Note that the rotation stage does not have an optical encoder. It is reset to 0 degrees at each program start in software. The motor controller can be configured for different levels of microstepping and motor current. Higher motor current provides more torque and less risk of missing steps. Load on the plate should not exceed 20 kg, and be centered around the rotation axis. Objects can be stabilised on the plate using e.g. modelling clay.

A calibration target is also part of the scanner. It was produced by printing a checkerboard in vector format, and gluing it onto a thick piece of float glass using spray adhesive. The target is asymmetrical, which is necessary to uniquely match chessboard corners in both cameras. The calibration target was designed to fill the scan objects space. If you need a smaller scan area, a smaller calibration target would be beneficial. In order to use a different chessboard, the field size and count paramters in the GUI configuration file (\texttt{{\textasciitilde}/.config/DTU/seema-scanner.conf}) need to be changed. Also note the minimal focus distance of the projector and cameras.

A calibration target is also part of the scanner. It was produced by printing a checkerboard in vector format, and gluing it onto a thick piece of float glass using spray adhesive. The target is asymmetrical, which is necessary to uniquely match chessboard corners in both cameras. The calibration target was designed to fill the scan objects space. If you need a smaller scan area, a smaller calibration target would be beneficial. 

The scanner GUI was developed using Qt, OpenCV and the Pointcloud Library (PCL). It enables the user to perform calibration of the scanner, and to aquire scan data. It is built in a modular fashion, to allow for new structured light strategies to be implemented. It is, however, supposed to be simple and stable, so please keep experimental builds in seperate SVN branches. 

The scanner GUI was developed using Qt, OpenCV and the Pointcloud Library (PCL). It enables the user to perform calibration of the scanner, and to acquire scan data. It is built in a modular fashion, to allow for new structured light strategies to be implemented. It is, however, supposed to be simple and stable, so please keep experimental builds in separate SVN branches. 

This class provides a fullscreen OpenGL context, and the ability to project any texture. The window/context creation is operating system dependant. It works very well on Linux with proprietary nVidia drivers, as found on the scan computer. In order to get a completely independant screen output, which does not interfere with the window manager, the projector needs to be set up as a seperate X screen in \texttt{xorg.conf}. The absolute position of this second X screen must provide a small gap to the primary screen. This gives a secondary screen, which is not recognized by Compiz (Unity in Ubuntu), but which can be accessed through the Projector class.

This class provides a fullscreen OpenGL context, and the ability to project any texture. The window/context creation is operating system dependant. It works very well on Linux with proprietary nVidia drivers, as found on the scan computer. In order to get a completely independent screen output, which does not interfere with the window manager, the projector needs to be set up as a seperate X screen in \texttt{xorg.conf}. The absolute position of this second X screen must provide a small gap to the primary screen. This gives a secondary screen, which is not recognised by Compiz (Unity in Ubuntu), but which can be accessed through the Projector class.

An abstraction from the individual industrial camera APIs was created, in order to ease replacement and enhance modularity. A concrete implementation for Point Grey cameras is provided. The program is currently designed for ''software triggering'' of the cameras. Due to substantial input lag in the projector and cameras, a certain pause must be made in program execution between projecting a certain pattern, and image capture. Close temporal syncronization of both cameras is achieved by calling the trigger method on both cameras, and collecting the images subsequently.

An abstraction from the individual industrial camera APIs was created, in order to ease replacement and enhance modularity. A concrete implementation for Point Grey cameras is provided. The program is currently designed for ''software triggering'' of the cameras. Due to substantial input lag in the projector and cameras, a certain pause must be made in program execution between projecting a certain pattern, and image capture. Close temporal synchronisation of both cameras is achieved by calling the trigger method on both cameras, and collecting the images subsequently.

The following guide explains the steps involved in calibration and aquisition of a $360^\circ$ scan of an object. 

The following guide explains the steps involved in calibration and acquisition of a $360^\circ$ scan of an object. 

Calibration parameters consist of camera focal lengths, central points, lens distortion parameters, camera extrinsics (their relative position and angles), and the location and orientation of the rotation stage axis. These parameters are stored in the GUI, but in most cases, it is recommended to perform a new calibration before aquiring new data. Also, the exact position of cameras may be altered to better fit the object, in which case recalibration must be done. The calibration parameters can be exported into a \texttt{*.xml} file through the top bar menu. The global coordinate system, in which everything is expresses coincides with the left camera.

Calibration parameters consist of camera focal lengths, central points, lens distortion parameters, camera extrinsics (their relative position and angles), and the location and orientation of the rotation stage axis. These parameters are stored in the GUI, but in most cases, it is recommended to perform a new calibration before acquiring new data. Also, the exact position of cameras may be altered to better fit the object, in which case recalibration must be done. The calibration parameters can be exported into a \texttt{*.xml} file through the top bar menu. The global coordinate system, in which everything is expresses coincides with the left camera.

	\item A number of calibration sets need to be aquired. The minium is 3 sets, and more is beneficial. The calibration pattern needs to be fully visible and equally bright in both cameras. The viewing angle must not be too shallow. The preset ''batch aquisition'' gives a reasonable number of calibration sets.

	\item A number of calibration sets need to be acquired. The minimum is 3 sets, and more is beneficial. The calibration pattern needs to be fully visible and equally bright in both cameras. The viewing angle must not be too shallow. The preset ''batch acquisition'' gives a reasonable number of calibration sets.

	\item After aquisition, individual calibration sets can be re-examined. Calibration parameters are automatically determined by clicking the ''Calibrate'' button. This procedure can take up to a few minutes. The terminal output will show recalibration errors, which measure the quality of calibration. 

	\item After acquisition, individual calibration sets can be re-examined. Calibration parameters are automatically determined by clicking the ''Calibrate'' button. This procedure can take up to a few minutes. The terminal output will show recalibration errors, which measure the quality of calibration. 

	\item The calibration result can be examined by changing to the ''Point Clouds'' tab in the GUI (see fig. \ref{fig:pointclouds0}). Left and right cameras are representated by colored coordinate systems (the viewing direction is the positive z-axis, y points down, x to the right). The rotation axis, as determined by the calibration procedure is shown as a white line section. 

	\item The calibration result can be examined by changing to the ''Point Clouds'' tab in the GUI (see fig. \ref{fig:pointclouds0}). Left and right cameras are represented by coloured coordinate systems (the viewing direction is the positive z-axis, y points down, x to the right). The rotation axis, as determined by the calibration procedure is shown as a white line section. 

	\item All data can be exported from the GUI program by means of the top bar menues. By exporting the point clouds into a folder, a \texttt{*.aln} is stored alongside these, which contains pose information in global coordinate space, which aligns the points clouds correctly and relative to each other.

	\item All data can be exported from the GUI program by means of the top bar menus. By exporting the point clouds into a folder, a \texttt{*.aln} is stored alongside these, which contains pose information in global coordinate space, which aligns the points clouds correctly and relative to each other.

Multiple point clouds can be merged into a single watertight mesh representation using Meshlab. Meshlab is available on the scanner computer, but also freely available for download for multiple platforms. The basic steps involved in merging and reconstructing are outlined below. The input data will consist of one or more sets of pointclouds aquired with the SeeMaLab GUI. Note that if multiple object poses are desired (for complex geometries/blind spots, etc.), it is recommended to close and restart the GUI for each pose, to clear the captured sequences and memory.

Multiple point clouds can be merged into a single watertight mesh representation using Meshlab. Meshlab is available on the scanner computer, but also freely available for download for multiple platforms. The basic steps involved in merging and reconstructing are outlined below. The input data will consist of one or more sets of pointclouds acquired with the SeeMaLab GUI. Note that if multiple object poses are desired (for complex geometries/blind spots, etc.), it is recommended to close and restart the GUI for each pose, to clear the captured sequences and memory.

	\item The PLY files do contain XYZ and RGB values for all points. You will need to compute normals, in order for the surface reconstruction to succeed. These normals can be estimated and consistently oriented by considering the camera viewpoint. Select all point cloud in turn and for each, choose ''Filters $\rightarrow$ Point Sets $\rightarrow$ Compute Normals for Point Set''. Make sure the ''Flip normals...'' checkbox is ticked (see fig. \ref{fig:meshlab1}). Suitable neighborhood values are in the order of $10$. You can visualize the estimated normals through the ''Render'' menu.

	\item The PLY files do contain XYZ and RGB values for all points. You will need to compute normals, in order for the surface reconstruction to succeed. These normals can be estimated and consistently oriented by considering the camera viewpoint. Select all point cloud in turn and for each, choose ''Filters $\rightarrow$ Point Sets $\rightarrow$ Compute Normals for Point Set''. Make sure the ''Flip normals...'' checkbox is ticked (see fig. \ref{fig:meshlab1}). Suitable neighbourhood values are in the order of $10$. You can visualise the estimated normals through the ''Render'' menu.

	\item After estimating normals for all point clouds in a set, choose ''Filters $\rightarrow$ Mesh Layer $\rightarrow$ Flatten Visible Layers''. Make sure to retain unreferences vertices, because at this point, none of the points will be part of any triangles (see figure \ref{fig:meshlab2}). This process will alter all coordinates by applying the pose transformation to all point clouds before merging them.

	\item After estimating normals for all point clouds in a set, choose ''Filters $\rightarrow$ Mesh Layer $\rightarrow$ Flatten Visible Layers''. Make sure to retain unreferenced vertices, because at this point, none of the points will be part of any triangles (see figure \ref{fig:meshlab2}). This process will alter all coordinates by applying the pose transformation to all point clouds before merging them.

If you have aquired multiple $360^\circ$ scans of your object in different position, proceed as above for each set. Then, you will need to align and merge these point cloud. Meshlab has manual coarse and automated ICP alignment integrated. Note that the automatic alignment procedure in Meshlab requires high quality point normal estimates for all point cloud to succeed. If this is not given, the alignment process will fail without warning or errors.

If you have acquired multiple $360^\circ$ scans of your object in different position, proceed as above for each set. Then, you will need to align and merge these point cloud. Meshlab has manual coarse and automated ICP alignment integrated. Note that the automatic alignment procedure in Meshlab requires high quality point normal estimates for all point cloud to succeed. If this is not given, the alignment process will fail without warning or errors.

	\item Load the point clouds of interest (''File $\rightarrow$ Import Mesh''). The imported point cloud will not be properly aligned. Open the alignment tool (a big yellow A tool button). See figure \ref{fig:meshlab4} for an image of this tool. ''Glueing'' in Meshlab means setting an initial rough alignment. You can ''glue'' the first mesh, and rough ''glue'' the others to it by selecting a small number (minimum 4) of surface point correspondences with the mouse. When all point clouds have been ''glued'', you can initiate automatic fine-alignment (groupwise ICP) by pressing ''Process''. A good alignment should be confirmed by selecting ''False colors'', and seeing a good mix of colors in overlap areas. 

	\item Load the point clouds of interest (''File $\rightarrow$ Import Mesh''). The imported point cloud will not be properly aligned. Open the alignment tool (a big yellow A tool button). See figure \ref{fig:meshlab4} for an image of this tool. ''Glueing'' in Meshlab means setting an initial rough alignment. You can ''glue'' the first mesh, and rough ''glue'' the others to it by selecting a small number (minimum 4) of surface point correspondences with the mouse. When all point clouds have been ''glued'', you can initiate automatic fine-alignment (group-wise ICP) by pressing ''Process''. A good alignment should be confirmed by selecting ''False colors'', and seeing a good mix of colours in overlap areas. 

The Poisson reconstruction algorithm does not keep color information. In order to obtain a colored mesh, one needs to reproject the per-point color information from the full point cloud to the mesh. This can be done in Meshlab through the ''Filters $\rightarrow$ Sampling $\rightarrow$ Vertex Attribute Transfer'' functionality. 

The Poisson reconstruction algorithm does not keep colour information. In order to obtain a coloured mesh, one needs to re-project the per-point colour information from the full point cloud to the mesh. This can be done in Meshlab through the ''Filters $\rightarrow$ Sampling $\rightarrow$ Vertex Attribute Transfer'' functionality.