DATA PROCESSING PROTOCOL User Manual
Data Processing Protocol
i
Contents Glossary ..........................................................................................................................iv 1.Introduction to Data Processing on KAARTA ............................................................... 1 1.1 Organization of This Manual .................................................................................. 1 1.2 Document Conventions ......................................................................................... 2 1.3 Requirements ........................................................................................................ 2 2. Post Scan Data Processing ......................................................................................... 3 2.1 Saving a Completed Point Cloud and Naming Conventions .................................. 3 2.1.1 To Name the Project Album ............................................................................ 3 2.1.2 To Name a Completed Point Cloud on the Post Scan Screen ........................ 3 2.1.3 To Rename a Completed Point Cloud ............................................................. 4 2.2 Opening Files on the KAARTA Contour ................................................................. 5 2.2.1 To Open CloudCompare from the Task Bar .................................................... 5 2.2.2 To Open CloudCompare from the File Management Screen .......................... 5 2.2.3 To Load a Point Cloud from Internal Storage ..................................................... 5 2.3 Preforming Completeness and Sanity Checks ...................................................... 6 2.3.1 To Check for Completeness ............................................................................ 6 2.3.2 To Check for Converging and Diverging Walls................................................ 6 2.3.3 To Check for Reflections ................................................................................. 7 2.3.4 To Check for Occlusions ..................................................................................... 7 3. Registration ................................................................................................................. 8 3.1 Registration Using Iterative Closest Point Algorithm.............................................. 8 3.1.1 To Register Automatically Using ICP .............................................................. 8 3.2 Alternative Registration of Partially Overlapping Clouds ........................................ 9 3.2.1 To Register Partially Overlapping Clouds using an Alternative Method .......... 9 4. Georeferencing ......................................................................................................... 10 4.1 Georeferencing a Point Cloud File Using Autodesk Revit .................................... 10 4.1.1 To Georeference in Autodesk Revit .............................................................. 10 4.2 Georeferencing a Point Cloud File Using CloudCompare .................................... 11 4.2.1 To Georeference in CloudCompare .............................................................. 11 5. Data Refining ............................................................................................................ 12 5.1 Data Import .......................................................................................................... 12
5.1.1 To Import .OBJ Files ..................................................................................... 12 5.1.2 To Import .PLY Files ..................................................................................... 12 5.2 Data Cropping...................................................................................................... 13 5.2.1 To Crop Point Clouds .................................................................................... 13 5.2.2 To Segment Point Clouds ............................................................................. 14 5.2.3 To keep the selected area ............................................................................. 14 5.2.4 To remove the selected area......................................................................... 14 5.3 Data Cleaning ...................................................................................................... 15 5.3.1 To Clean Data Using SOR Filter ................................................................... 15 5.3.2 To Clean Data Using the Noise Filter ............................................................ 16 6. Data Compression..................................................................................................... 17 6.1 Subsampling the Point Cloud in Cloud Compare ................................................. 17 6.2 Spatial Subsampling Method ............................................................................... 18 6.2.1 To Subsample Using the Spatial Method ...................................................... 18 6.3 Random Subsampling Method............................................................................. 19 6.3.1 To Subsample Using the Random Method.................................................... 19 6.4 Octree Subsampling Method ............................................................................... 20 6.4.1 To Subsample Using the Octree Method ...................................................... 20 6.5 Lossless Data Compression ................................................................................ 20 7. Data Export ............................................................................................................... 21 7.1 To Import a .LAS or .LAZ Point Cloud to CloudCompare .................................... 21 7.2 To Export a .PLY Point Cloud Using CloudCompare ........................................... 21 7.3 To Import a .PLY Point Cloud to Autodesk MeshMixer ........................................ 22 7.4 To Export a .OBJ File Using Autodesk MeshMixer .............................................. 22 7.5 To Import a .OBJ Point Cloud to Autodesk Recap ............................................... 22 7.6 To Export a .RCS file Using Autodesk Recap ...................................................... 23 Appendix I – Post Scan Error Checking .......................................................................... iii Appendix II – CloudCompare ..........................................................................................vi Appendix III – Georeferencing ........................................................................................ vii Index ............................................................................................................................. viii
Glossary Accuracy
The degree of conformity of a measured or calculated value compared to the actual value. Accuracy relates to the quality of a result and is distinguished from precision, which relates to the quality of the operation by which the result is obtained (ASPRS Guidelines for Procurement)
Converging Walls
a type of error that occurs from the sensor drift, characterized by opposite walls moving towards each other, when they remain parallel in the physical space.
Coordinate systems
a system that uses one or more numbers, or coordinates, to uniquely determine the position of the points or other geometric elements on a manifold such as Euclidean space.
Diverging Walls
a type of error that occurs from the sensor drift, characterized by opposite walls moving apart from each other when they remain parallel in the physical space.
Entity
The content of the point cloud file, they can be data, a model, or mesh point cloud.
Intensity
A value indicating the amount of laser light energy reflected back to the scanner
Laser Scans
The capturing of building measurements by combining controlled steering of laser beams with a laser rangefinder. By taking a distance measurement at every direction the scanner rapidly captures the surface shape of objects, buildings and landscapes.
Light Detection and Ranging (LiDAR)
a remote sensing method that uses light in the form of a pulsed laser to measure ranges (variable distances) to the Earth.
Noise
unnecessary extra points that affect the quality and size of the point cloud. they result from random errors during the scanning process and are unavoidable.
Occlusions
Point Cloud
Gaps in scan data caused by temporary obstructions or inadequate scanner occupation positions. Overlapping scans and awareness of factors causing data shadows can help mitigate data voids. Some data voids are caused by temporary obstructions such as pedestrians and vehicles.
a set of data points in space, where the 3D point data collected by a laser scanner from a single observation session. A point cloud may be merged with other point clouds to form a larger composite point cloud. Data from within a point cloud may be used to produce traditional survey products. Point clouds can be specified as a deliverable.
Point Density
The average distance between XYZ coordinates in a point cloud, typically at a specified distance from the scanner. The point density specified by the client or selected by the contractor should be understood as the maximum value for the subject in question and should be dense enough to achieve extraction of detail at the scales specified for the project.
Survey Point
a reference point of the intersection of two property lines to a location in the physical world such as, a geodetic survey marker.
Target
the physical point in which the user connects the survey point to through geographic coordinates.
Reflection
a type of error that occurs during the scanning process, that causes points to appear outside the scanned area. It is caused by reflective or highly glossy surfaces.
Data Processing Protocol
v
1 1.Introduction to Data Processing on KAARTA Processing Point Clouds on KAARTA details a process developed at Algonquin College to take raw scans captured by the KAARTA Contour, and change them into a file that can be used to model or maintain buildings.
In This Section • • • •
Data Processing on KAARTA Organization of this Manual Document Conventions Requirements
As a part of the Department of Nation Defence’s (DND) 2019 inventory of assets, service members will scan physical assets with a KAARTA Contour. The scans generated by the KAARTA will be collected for data processing and refinement before finally being used to build 3D computer models of the assets. The models can be used to predict maintenance schedules, material requisitions and assess the potential of renovations on all spaces inventoried. The KAARTA Contour is a very powerful tool capable of capturing millions of data points in a single scan. However, it is just a machine, it is not smart, and it cannot make decisions about the data that it captures. To use the captured data in a useful way, a person must judge the accuracy of the data, the completeness of the data and convert the data into useful files. It is important to control the format of each file as it passes from each piece of software to the next. The rapid expansion of the BIM field has led to many competing software and file formats. This manual takes great care to ensure that files passed from one step to the next are in a format that the next step can use. This manual guides a new user through the evaluation and cleaning of captured data, the referencing of completed point clouds, and the exporting of referenced files.
1.1 Organization of This Manual This user manual is organized in five main sections, with a reference section at the end. Each section begins with an overview that outlines the section’s content, and is followed by instructions. The section pages are displayed in a two-column format, with the lefthand column containing overviews, followed by procedures and images to reinforce the procedures. In all main sections, on the right-hand column there is a small table displaying the number and the contents of the section.
Data Processing Protocol
1
1.2 Document Conventions The following table illustrates the conventions used throughout this user manual, with a brief description of each icon. Icon
Convention
Note
Description A note provides additional information such as: details about a step, the function of a part/ feature or why this step is important. Paying attention to notes helps completing a procedure successfully.
Tip
A tip provides an alternate method of completing a procedure, or advice that may be useful in the future.
Result Statement
A result statement describes the observable outcome of a procedure, indicating its success.
1.3 Requirements Complete access to following software is required to successfully go through with the Data Processing Protocol: • • • •
CloudCompare Autodesk Revit Autodesk MeshMixer Autodesk Recap
Data Processing Protocol
2
2. Post Scan Data Processing Post scan processing is a procedure which organizes and identifies problems with point cloud files so that they may be effectively used by the team. The procedure requires that you save completed point clouds with a standardized name, check that the point clouds are complete, and check that they are accurate.
2.1 Saving a Completed Point Cloud and Naming Conventions Naming point cloud files in an informative and consistent way is important so that they can be found and used by other team members. This section describes naming the project album, naming complete point clouds, and renaming mislabelled or revised point clouds. 2.1.1 To Name the Project Album 1. On the main gallery screen click on the album. 2. Click on Rename in the top navigation bar. 3. Type the name of the album in the following format: YYMMTNo_ProjectName replacing the placeholders with: YY MM T No ProjectName
-
The last two digits of the year the project started The month the project started The type of area scanned. “I” for internal or “E” for external The unique project number assigned to the project A descriptive name of the asset scanned
4. Click Done to commit the name. 2.1.2 To Name a Completed Point Cloud on the Post Scan Screen 1. On the post scan click Rename on the top navigation bar (see Figure 1). 2. Type the name of the file in the following format: YYMMDD_FloorCode_RoomName_RevisionNo replacing the placeholders with: YY MM DD FloorCode
-
RoomName RevisionNo -
The last two digits of the year the point cloud was captured The month the point cloud was captured The day the point cloud was captured Two-character floor identification code (see Table 1: Floor Identifier Codes) A descriptive name of the room scanned. Two-digit revision identification number.
3. Click Done to commit the name.
Data Processing Protocol
3
Figure 1: Post Scan screen.
Table 1. Floor Identifier Codes
Identifier Code B2 B1 P2 P1 GF FF SF PH
Floor Name Basement Level Two Basement Level One Parking Level Two Parking Level One Ground Floor First Floor Second Floor Penthouse
2.1.3 To Rename a Completed Point Cloud 1. On the main gallery screen, click the album which contains the misnamed file 2. Select the file to be renamed and click Rename in the top navigation bar 3. Type the new file name in the following format: YYMMDD_FloorCode_RoomName_RevisionNo replacing the placeholders with: YY MM DD FloorCode
-
RoomName RevisionNo -
The last two digits of the year the point cloud was captured The month the point cloud was captured The day the point cloud was captured Two-character floor identification code (see Table 1: Floor Identifier Codes) A descriptive name of the room scanned Two-digit revision identification number
4. Click Done to commit the name
Data Processing Protocol
4
2.2 Opening Files on the KAARTA Contour The KAARTA Contour comes preloaded with the application CloudCompare for point cloud viewing and processing. To view any point cloud on the KAARTA Contour requires the use of CloudCompare. This section details opening CloudCompare from the task bar, opening CloudCompare from the file management screen and loading point cloud files in CloudCompare. 2.2.1 To Open CloudCompare from the Task Bar 1. From any screen, click on the CloudCompare
icon on the task bar
2.2.2 To Open CloudCompare from the File Management Screen 1. From the main gallery screen, click on an album’s thumbnail image. 2. Click on the point cloud 3. Click on File Mgmt. on the side navigation bar 4. Click CloudCompare to launch the application (See Figure 3)
Figure 2: KAARTA Contour’s file management screen.
2.2.3 To Load a Point Cloud from Internal Storage 1. With CloudCompare open, click File > Open 2. In the bottom right corner of the Open File(s) window, click the drop-down menu and select the scan’s file extension 3. Navigate to the location where the point cloud is saved 4. Choose the point cloud file 5. Click Ok
Data Processing Protocol
5
2.3 Preforming Completeness and Sanity Checks Completeness and sanity checks are performed on captured point clouds to ensure that every room was captured using the scanner and that the data captured is usable. Each of these checks is to be performed on site after each scan is complete using the KAARTA Contour’s build in point cloud viewing function. Four checks must be performed on the data after capture: • • • •
Completeness check Converging and diverging wall check Reflection check Occlusion check
2.3.1 To Check for Completeness 1. Open CloudCompare and load the point cloud 2. Click Set top view
located on the left-hand tool bar
3. Click Global Zoom located on the left-hand tool bar 4. Check the point cloud for holes and missing rooms 5. If the point cloud is missing an area use the KAARTA Contour’s SLAM function to scan the room and add it to the point cloud (See Data collection Protocol Section) 2.3.2 To Check for Converging and Diverging Walls See Appendix I - Post Scan Error Checking for examples of converging and diverging walls. 1. 2. 3. 4.
Open the point cloud in CloudCompare Left-click and hold the mouse to rotate and explore the point cloud Right-click and hold the mouse to pan and explore the point cloud Check each pair of opposite walls in the point cloud. If a pair does not remain equal distance from each other check the corresponding scanned area. 5. Check for features that appear from multiple angles. If an object appears multiple times or its sides do not align, check the corresponding scanned area. 6. If the point cloud disagrees with the scanned area, rescan the area using the KAARTA Contour’s SLAM Function. (See Data Collection Protocol Section)
Data Processing Protocol
6
2.3.3 To Check for Reflections See Appendix I - Post Scan Error Checking for examples of reflections and information on correcting them. 1. 2. 3. 4. 5. 6. 7.
Open the scan in CloudCompare Left-click and hold the mouse to rotate and explore the point cloud Right-click and hold the mouse to pan and explore the point cloud Check the outer edges of the scan for points that do not correspond to a room If a reflection is identified, close CloudCompare Navigate to the point cloud file and click on it Click the Notes field on the Scan Detail screen and enter a note describing the location of the reflection (See Figure 4)
Figure 3: Scan Detail screen with the notes field highlighted.
8. Click Done to save the note
2.3.4 To Check for Occlusions See Appendix I - Post Scan Error Checking for examples of occlusions and information on correcting them. 1. 2. 3. 4.
Open the scan in CloudCompare Left-click and hold the mouse to rotate and explore the point cloud Right-click and hold the mouse to pan and explore the point cloud Check around furniture and in room with many features for areas with noticeably lower point density 5. Check halls for smears or shadows caused by people moving though the space 6. Rescan areas with occlusions using the KAARTA Contour’s SLAM Function (See Data Collection Protocol Section) Data Processing Protocol
7
3 In This Section
3. Registration The process of consistently aligning various 3D point cloud data views into a complete model is known as registration. Its goal is to find the relative positions and orientations of the separately acquired views in a global coordinate framework, such that the intersecting areas between them overlap perfectly. Every set of point cloud dataset is acquired from different views, therefore a system that can align them together into a single point cloud model is required, so that subsequent processing steps such as segmentation and object reconstruction can be applied. Registration in CloudCompare can be done through Fine Registration with Iterative Closest Point (ICP) algorithm and an alternative method of registering partially overlapping clouds.
•
Registration
•
Automatic Registration
•
Registering Partially Overlapping Cloud
•
Georeferencing
•
Georeferencing in Revit
•
Positioning Methods
3.1 Registration Using Iterative Closest Point Algorithm The Iterative Closest Point algorithm is the only automatic registration method, that would finely register two entities. 3.1.1 To Register Automatically Using ICP 1. Select the two entities (clouds and/or meshes) that you want to register Hold down the CTRL (Windows) / Command (Mac) key on your keyboard to select multiple non-sequential files. 2. Click on the Fine Align the registration
icon located in the main toolbar at the top, to begin
A Clouds Registration window will appear on the screen. 3. Under Role assignation, decide which entity will be the Data and which one will be the Model 4. Click on the Swap button to put the default role assignation Data and Model are assigned colours in the Cloud Registration window, that correspond to the entities displayed in 3D view.
Data Processing Protocol
8
3.2 Alternative Registration of Partially Overlapping Clouds This method is an old way of registering two partially overlapping clouds (i.e. prior to version 2.6.1). 3.2.1 To Register Partially Overlapping Clouds using an Alternative Method 1. In the 3D view, right-click on the entity and choose Rotate 2. In the 3D view, left-click on the entity and choose Translate
Hit the space bar to pause or un-pause the transformation mode any time.
3. Click on the Clone icon located in the toolbar at the top to clone the data cloud 4. Uncheck the box in the DB Tree tab, to hide the original data
4. DB Tree tab located on the left-hand side
5. Select the new version of the data cloud, and click on the segmentation tool icon located in the toolbar at the top, allowing only the points clearly overlapping with the model cloud 6. Apply Fine Registration (ICP) algorithm to the data cloud. Refer to 3.1 Registration Using Iterative Closest Point Algorithm
CloudCompare will output the resulting transformation matrix in the Console.
7. Copy the transformation 8. Check the box in the DB Tree tab, to make the original data visible again 9. Click Edit > Apply Transformation and paste the transformation in the first tab
Data Processing Protocol
9
4 In This Section
4. Georeferencing Georeferencing is the process of rotating, translating, and transforming a point cloud into a real-world spatial reference system, that connects to coordinates. Mention Datum
4.1 Georeferencing a Point Cloud File Using Autodesk Revit While inserting a point cloud file in Revit, positioning can be adjusted to apply desired coordinates. This process can be done through four methods of positioning:
•
Georeferencing
•
Georeferencing Using Autodesk Revit
•
Georeferencing Using CloudCompare
4.1.1 To Georeference in Autodesk Revit 1. Click on the Point Cloud icon, to link a point cloud 2. Navigate to where the point cloud is saved and select it 3. From the Positioning drop-down menu, choose a positioning method. Refer to Appendix II – CloudCompare 4. Click Open
6. Link Point Cloud window
5. Point cloud with a hypothetical survey point highlighted
5. Select a survey point 6. 7. 8. 9.
Using the Move tool, place the survey point on the target In the Manage menu, click on Coordinates > Specify Coordinates at Point In the Specify Shared Coordinates dialogue box, enter the coordinates Click OK apply changes
Data Processing Protocol
10
4.2 Georeferencing a Point Cloud File Using CloudCompare CloudCompare is a program for processing point clouds (such as those obtained using a 3D scanner). It also processes triangular meshes and calibrated images. 4.2.1 To Georeference in CloudCompare 1. Select an entity 2. Click Edit > Global Shift/ Scale Global Shift / Scale dialogue box will appear on the screen
7. Global Shift/scale window
The values in the turquois frames are automatically updated when the Shift/Scale values are changed.
3. Adjust the Shift and Scale values in their designated fields 4. Click Yes to apply changes
Data Processing Protocol
11
5 In This Section
5. Data Refining
•
Data Refining
Data refining is to convert raw data into usable and workable study material. Cloud Compare is used to refine the rough scanned data in this section.
•
Data Import
•
Data Cropping
•
Data Cleaning
•
Statistical Outliner Removal Filter
•
Noise Filter
In this section, data refining is done through cropping, segmentation and performing SOR filter as well as Noise filter to remove unwanted material. To refine the data is very important to have accurate modeling, the scanned data contains lot of noise and under-sampling. And so, it produces rough surfaces, gaps, holes and irregular boundaries. It is important to refine data before it is utilized for advanced study.
5.1 Data Import Importing allows you to refine files with various formats such as: .bin, ASCII, .las, .ply, .7z and .obj among other formats. 5.1.1 To Import .OBJ Files 1. File > Open 2. Choose the .obj file from the Open File window 3. Click Open 5.1.2 To Import .PLY Files 1. File > Open 2. Choose the .ply file from the Open File window PLY File Open window will appear on the screen. 3. Click Apply without making any changes to the Default settings 4. Click Open
You can Drag and Drop files to CloudCompare for a quick import.
Data Processing Protocol
12
5.2 Data Cropping Removing unnecessary outer data of photography or videography is an essential part of refining data, and can also be used to reduce the point cloud size. In CloudCompare cropping creates a new entity without modifying the original entity. A properly cropped point cloud should look similar to the image below, where the white portion is the cropped data. Unwanted material can be removed by cropping and segmenting.
8. example of a cropped point cloud
5.2.1 To Crop Point Clouds 1. Click Edit > Crop A Crop window will appear on the screen. 2. From the first drop-down menu on the left, choose Center
Checking the Keep Square box will lock the cubical cropped object’s dimensions with an equal length and width 3. Enter the desired size value of the Point Cloud in the X, Y, Z fields 4. Click Ok to apply changes
Data Processing Protocol
13
5.2.2 To Segment Point Clouds 1. Click Edit > Segment The Segment Tool Tab will appear
2. Click on the Polygon icon 3. Left-click the point to begin the shape selection Once you are in selection mode you cannot rotate the object.
4. Keep left-clicking around the selection area until your selection is done. 5. Right-click to stop selecting
9. selected area (green) in a point cloud
5.2.3 To keep the selected area • Click on the Segment In
icon
10. Selection excluding the outer portion
5.2.4 To remove the selected area • Click on the Segment Out
icon
11. Selection excluding the inner portion
Data Processing Protocol
14
5.3 Data Cleaning Fixing or removing incorrect, incomplete, improperly formatted or duplicated data is a crucial part of data refining. In this section, data cleaning is done through Statistical Outliner Removal (SOR) Filter, which computes first the average distance of each point to its neighbors (considering k nearest neighbors for each - k is the first parameter), and the Noise Filter considers the distance to the underlying surface instead of neighbouring points. Before
After
Before
13. SOR filter result
After
12. Noise filter result
5.3.1 To Clean Data Using SOR Filter 1. Tools > Clean > SOR Filter A Statistical Outliner Removal window will appear on the screen. 2. Enter the value of the number of points in the first field
14. Statistical Outliner Removal window
3. Enter the value of the standard deviation multiplier threshold in the second field 4. Click OK to apply changes
Data Processing Protocol
15
5.3.2 To Clean Data Using the Noise Filter 1. Click Tools > Clean > Filter Noise
A Filter Noise window will appear on the screen. 2. Under Neighbors, check the Radius radio button
15. Filter noise window
3. Under Max Error, enable the Relative radio button and assign a value in the field below it 4. Check the Remove isolated points box 5. Click OK to apply changes
Data Processing Protocol
16
6 In This Section
6. Data Compression Compressing data reduces the bits needed to represent scanned data, and in turn saves storage capacity, speeds up the file sharing process, and decreases hardware and bandwidth related costs. Data can be compressed using the following four methods: • • •
Reducing point density by subsampling the point cloud Removing unnecessary points by cropping the point cloud Lossless data compression
•
Data Compression
•
Subsampling
•
Subsampling Methods
6.1 Subsampling the Point Cloud in Cloud Compare subsampling a point cloud in CloudCompare allows decreasing number of points, and subsequently reducing the file size. The values and intensity of a subsample can be adjusted according to the user, based on the purpose of the scan. Several Subsampling methods are available such as: • Spatial • Random • Octree
Data Processing Protocol
17
6.2 Spatial Subsampling Method In spatial mode, it is essential to set a minimal distance between each point. CloudCompare will then pick points from the original cloud, to prevent points in the output cloud from being closer to one another. Setting the sampling parameters defines the values. 6.2.1 To Subsample Using the Spatial Method 1. Click File > Open Make sure that the point cloud is selected before completing the next step.
2. Click on the subsample point cloud icon top
located in the main toolbar at the
16. Cloud subsampling window (Spatial Method)
3. Click on the method drop down menu, under Sampling parameters and select Space 4. Check the Use active SF box 5. Enter the value of the Spacing value in the designated fields (e.g. 10.000000) 6. Click OK to complete the subsampling A Build dialogue box will display the progress of the spatial subsampling.
Data Processing Protocol
18
6.3 Random Subsampling Method In random mode, CloudCompare will simply pick the specified number of points in a random manner. 6.3.1 To Subsample Using the Random Method 1. Click File > Open Make sure that the point cloud is selected before completing the next step.
2. Click on the subsample point cloud icon top.
located in the main toolbar at the
3. Click on the method drop down menu, under Sampling parameters and select Random 4. Slide the tab to adjust the spacing value 5. Click OK to complete the subsampling
17. Cloud Subsampling window (Random Method)
Data Processing Protocol
19
6.4 Octree Subsampling Method The octree mode allows you select a level of subdivision of the octree at which the cloud will be simplified. In each cell of the octree, the nearest point to the octree cell center is kept. For each input cloud, a new subsampled cloud is created with the original one deactivated. A subsampled cloud keeps the feature of its source, and is a subset of the input cloud (the original points are not displaced). 6.4.1 To Subsample Using the Octree Method 1. Click File > Open
Make sure that the point cloud is selected before completing the next step.
2. Click on the subsample point cloud icon top.
located in the main toolbar at the
Cloud Subsampling dialog box will appear on the screen. 3. Click on the method drop down menu, under Sampling parameters and select Octree 4. Enter the value of the Subdivision level in the designated field (e.g. 10) 5. Click OK to complete the subsampling
6.5 Lossless Data Compression This method involves zipping the processed point cloud file that is ready to be shared using compression software such as: WinRAR, 7zip, WinZip, etc.
Data Processing Protocol
20
7 In This Section
7. Data Export Data export is the final step in the point cloud processing protocol. Modeling in Autodesk Revit requires that the point clouds be exported in the .RCS or .RCP file formats. Exporting point clouds for use with Revit requires three steps; starting with a .LAZ or .LAS point cloud you must convert it to a .PLY point cloud using CloudCompare. PLY point clouds must then be converted to .OBJ files using Autodesk MeshMixer. The final conversion is from .OBJ to the final .RCS file using Autodesk Recap.
•
Data Export
•
Exporting Using CloudCompare
•
Exporting Using Autodesk MeshMixer
•
Exporting Using Autodesk Recap
7.1 To Import a .LAS or .LAZ Point Cloud to CloudCompare 1. With CloudCompare open click File > Open. 2. In the bottom right corner of the Open File(s) window, click the dropdown menu and select the scan’s file extension 3. Navigate to the location where the point cloud is saved 4. Double-click the point cloud file to import it
7.2 To Export a .PLY Point Cloud Using CloudCompare 1. 2. 3. 4.
Click File > Save In the DB Tree tab select the desired point cloud In the Save as type drop-down menu, choose PLY mesh (*.PLY) Type the name the file in the field marked File name For file naming conventions see 2.1 Saving a Completed Point Cloud and Naming Conventions
5. Click Save
Data Processing Protocol
21
7.3 To Import a .PLY Point Cloud to Autodesk MeshMixer 1. 2. 3. 4. 5.
Launch Autodesk MeshMixer Click File > Import Navigate to the location where the point cloud file is saved Select the point cloud file Click Open
7.4 To Export a .OBJ File Using Autodesk MeshMixer 1. In Autodesk MeshMixer click File > Export 2. In the Export Mesh window, type the file name in the field marked File name For file naming conventions see 2.1 Saving a Completed Point Cloud and Naming Conventions 3. In the Save as type dropdown menu, choose OBJ Format (*.obj) 4. Navigate to the point cloud’s place in the file structure 5. Click Save
7.5 To Import a .OBJ Point Cloud to Autodesk Recap 1. 2. 3. 4.
Launch Autodesk Recap Click New Project Click Import point cloud Name the project For file naming conventions see 2.1 Saving a Completed Point Cloud and Naming Conventions
5. Click select files to import 6. In the Import Files window, select the point cloud and click OK 7. After the file is fully loaded click launch project
Data Processing Protocol
22
7.6 To Export a .RCS file Using Autodesk Recap 1. Hover the mouse over the Home button, then over the revealed Save button 2. Click the “Export” button
Figure 18: Autodesk Recap with the Home menu open and the “Export” button highlighted.
3. In the Select File Name for Export window, type the file name in the field marked File name For file naming conventions see 2.1 Saving a Completed Point Cloud and Naming Conventions 4. In the Save as type dropdown menu, choose Unified RCP (*.rcp) 5. Navigate to the point cloud’s designated place in the file structure 6. Click Save
Data Processing Protocol
23
Appendix I – Post Scan Error Checking Sanity Checks Sanity checks are visual checks that must be done to point cloud files to ensure that the data contained corresponds with the area that was scanned. They rely on the user to find areas where the collected data is different from the space that was scanned. Users should check files for each of the following error types: • • •
Converging and Diverging Walls Reflections Occlusions
If any of these errors are present in the scan, the area should be rescanned closely following the Data Collection Protocols User Manual. Converging and Diverging Walls Converging and diverging walls are a type of scan error where the point cloud shows that opposite walls run together or bow apart from each other when they are parallel in the physical space. Converging and diverging wall errors are caused by sensor drift, or the scan being taken too fast. To determine if a scan has converging walls look for pairs of walls that you know are parallel in the space but approach each other in the scan. I can be useful to
Figure 19: Top down image of a point cloud with converging walls. Notice how in the highlighted area the walls are farther apart at one end then at the other. If an inspection of the area shows that the walls do not separate the area should be rescanned.
Data Processing Protocol
iii
Figure 20 :Top down image of a row of windows on diverging walls. The sides of the center two windows have noticeably separated due to a diverging wall.
To correct a scan with converging walls, rescan the affected areas following a different path. Converging walls can be corrected in the Modeling stage.
Reflections Reflections are a type of scan error where the point cloud shows extra rooms outside of the scanned area. Reflections are caused by reflective surfaces, especially mirrors and glossy finishes. To determine if a point cloud has reflections in it look at edges of the scanned area (Figure 9) and determine if there are points that do not correspond to a physical space.
Figure 21:Top down view of a point cloud with a reflected extra room.
If a point cloud has reflections, they will need to be cropped out during data cleaning.
Data Processing Protocol
iv
Occlusions Occlusions are a type of scan error where the scan shows phantom shapes, smears, or areas with very little point density. Occlusions are caused by objects in the scanning area blocking the scanner from seeing the area behind them.
Figure 22: Top down view of a point cloud where a piece of furniture has occluded the scan and reduced the density of the cloud behind it.
To determine if a scan has occlusions look for areas of low point could density. Pay extra attention to small rooms with many features or a lot of furniture. Occlusions can also be caused by people walking though the scan. Look in hallways for smears or partial images of people. To prevent occlusions path around large objects, if people are present in the scan the area can be rescanned or the points cropped out.
Data Processing Protocol
v
Appendix II – CloudCompare About Cloud Compare Software Cloud Compare Software is an open source program written by Daniel GiradeauMonteau in 2004 in France. Many others contributed in PlugIns and other aspects. It is a 3D Point cloud and triangular mesh processing software. It has been originally designed to perform comparisons between 3D Point clouds. You register (Join two scans together. It is easy when scans are similar but difficult when they are dissimilar), edit, and transform data on it. You can download it from http://www.danielgm.net/cc/release/. You can learn more about the software through tutorials and help. It is available on Window, Mac, and Linux. It is fairly accessible and downloadable.
Data Processing Protocol
vi
Appendix III – Georeferencing Georeferencing in Revit While inserting a point cloud file in Revit, we can position to apply desired coordinates in the scan. There are different methods for positioning: Positioning Methods •
Auto – Center to Center Positioning Revit places the center of the point cloud bounding box at the center of the bounding box of the model. If most of the model is not visible, this center point may not be visible in the current view. To make the point visible in the current view, set the zoom to Zoom View to Fit. This function centers the view on the Revit model.
•
Auto – Center to Origin Positioning Revit places the point cloud's world origin, i.e. (0,0,0) point, at the Revit project origin that can be seen as a project base point in the site plan. If your Project North is rotated, Revit will also rotate the point cloud so that the point cloud's north direction (0,1,0) maps to the current Project North. It makes sense to use this option if your point cloud is sampled with respect to the known point and known direction in your model or on your site. Auto – By Shared Coordinates Positioning Revit assumes that the coordinates in the point cloud file are specified in the shared coordinate system used in your model. As a result, the point cloud origin will be placed at the origin of the shared coordinates, that can be accessed through the Survey Base Point. The point cloud will be oriented so that the north direction in the cloud file (0,1,0) will be mapped to the True North of the Revit model.
•
•
Auto – Origin to Last Placed Positioning Revit places a point consistently in relation to the previously imported point cloud; this option becomes enabled after one-point cloud is inserted, this first cloud can be moved to align it properly with the model elements. If additional point clouds are created on the same site and in the same coordinate system as the first one, it is recommended to use this option to insert the additional point clouds. New point clouds will then be correctly placed with respect to the first one. If the coordinates of the target are obtained later, these coordinates can be applied in the scan in Revit, and to the targets.
Data Processing Protocol
vii
Index
F files. See point cloud ii
A
G
accuracy 8
georeferencing 11,12
I C
Importing 22, 23, 24
converging walls
L
checking 6
.LAS Importing 22
Identifying 6 cropping
.LAZ. See .LAS
segment in 15
N
segment out 15
naming albums 3
D
naming conventions 3
data cleaning
naming files. See naming albums
noise filter 16, 17
O
Statistical Outliner Removal filter 16
.OBJ exporting 22
data compression
importing 23
octree method 21 random method 20
occlusions checking 7
spatial method 19
identifying 27
data refining cleaning 16
organization. See naming conventions
cropping 14
P
segmenting15
.PLY
diverging walls 7, 25
exporting 22
E
importing 23
exporting
point cloud
.OBJ 22
exporting 22, 24
.PLY 22
importing 22, 23, 24
.RCS 24
positioning methods 29 precession. See accuracy
Data Processing Protocol
viii
R .RCS exporting 24 reflections 7, 26 renaming files 4
S scaling factor 8 subsampling See also data compression random method 20 spatial method 19 octree method 21
Data Processing Protocol
ix