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Be aware that the higher the quality setting, the longer the processing time. Consider using lower setting for large data sets i. Suggested settings are shown below;. The approximate maximum number of points in a dense cloud is calculated by multiplying the number of megapixels of the camera e.
It is impossible to correlate all points and thus the actual number of pixels in a point cloud is generally only a fraction of the total possible 0. The resulting dense point cloud is similar to the key point, low density cloud but it contains exponentially more 3-D information 39,, 3-D pixels!
The next step is to filter any spurious points or outliers which are not representative of the surface being modeled in 3-D. The type of surface greatly affects the ability and accuracy of filtering points. Conversely in heavily treed areas or where there is high frequency, large-scale changes in elevation e. Total number of created sparse points was 42, These percentages directly affect the total number of points in the dense cloud Marcel, Fig.
For this test, the author intentionally skipped a trial of the Ultra High sub-setting because once the computer began processing, it provided an estimated processing time of more than two weeks. Thus, this setting is not practical for typical archaeological projects, which are usually conducted without the services of a super computer. Accuracy: High setting on Build Dense Cloud process. Total number of the points is 38,, Accuracy: Medium setting on Build Dense Cloud process. Total number of the points is 9,, Accuracy: Low setting on Build Dense Cloud process.
Total number of the points is 2,, Accuracy: Lowest setting on Build Dense Cloud process. Total number of the points is , Image: Yamafune Another sub-setting is Depth Filtering. This filter has four selectable options: Disabled, Mild, Moderate, and Aggressive. The Aggressive option means that if the object is monotone, PhotoScan will recognize it as void space and will not create points within that space Fig. Selecting the Mild option prompts the program to recognize monotone areas as objects, and to create points within those spaces Fig.
For example, the Aggressive depth filter does not recognize the surface of a white board as an object, and it creates a hole in its photogrammetric model; on the other hand, the Mild setting may recognize blue sky as a blue object, and thus create point clouds in what would otherwise be void space. Therefore, Mild works well on a monotone surface, and Aggressive works well with images that contain actual void spaces.
Moderate filtering works between the previous two filtering options Fig. Disabled mode does not recognize depth; in other words, if an object has a monotone colored surface, PhotoScan does not regard it as a surface and creates void spaces Fig.
Build Dense Cloud process with Depth filtering: Aggressive setting. Build Dense Cloud process with Depth filtering: Mild setting. Build Dense Cloud process with Depth filtering: Moderate setting.
Build Dense Cloud process with Depth filtering: Disabled setting. Surface Type has options Height Field and Arbitrary; when the subject of photogrammetric modeling is a flat structure, such as a level field ex. In general, Dense Cloud is preferable since it generates more minute details. Build Mesh process with Surface Type: Arbitrary setting.
Build Mesh process with Surface Count: High setting. Total face count is 1,, Build Mesh process with Surface Count: Medium setting. Total face count is , Build Mesh process with Surface Count: Low setting.
Image: Yamafune Another sub-setting is Interpolation. This sub-setting has three options: Disabled, Enabled default , and Extrapolated. Disabled accurately reconstructs meshes based on point clouds Fig. Alternatively, Enabled measures a certain diameter from each point and creates meshes as if the separated points were located within the diameter; in short, it automatically fills small holes Fig.
Extrapolated creates meshes between separate points and aggressively fills holes, including large holes Fig. Consequently, if the purpose of a photogrammetry project is to acquire accurate archaeological data, the Disabled Interpolation setting is preferred. On the other hand, when visualization is the main goal of the photogrammetric modeling, Enabled is the better choice.
Build Mesh process with Interpolation: Disabled setting. Build Mesh process with Interpolation: Enabled setting. Build Mesh process with Interpolation: Extrapolated setting.
Image: Yamafune The last sub-setting is Point Classes. This sub-setting is for GIS-based mapping software; it allows point clouds to be imported from mapping software. They can be classified into different categories, such as Ground, Vegetation, Building, Water, etc. This sub-setting allows PhotoScan to read the categories assigned by GIS mapping software, and then create meshes of chosen point types.
In this process, PhotoScan creates a photomosaic and places it on the surface of the meshes. The first sub-setting is Mapping Mode; this applies methods of UV mapping of created textures. UV mapping creates a 2D atlas, or a canvas on which textures can be projected. The default setting is Generic; with this option, PhotoScan automatically chooses the best photos based on cubic projection and composes a UV of meshes into texture atlases in order to convert the 3D structure of meshes into a 2D image format of created textures.
This mode is preferred for more complicated models that have 3D structures Fig. Orthophoto creates a photomosaic from a projection plane generally, a top view plane and projects a created photomosaic onto the meshes Fig.
Differently from Orthophoto, Adaptive Orthophoto detects vertical faces and creates textures independently for those side meshes Fig. Spherical mode creates a UV map for ball-like, spherical structures. A texture atlas is a 2D plane; therefore, texturing a spherical shape requires a unique mapping projection similar to world maps and globes.
However, this mode is only suitable for spherical structures Fig. Single Photo creates texture based on one photo; a photo can be chosen from any photo in the active Chunk. Keep UV is for importing a UV map created in different software. Many modeling softwares allow the creation of texture on meshes. This process also includes creating UV maps.
Build Texture process with Mapping Mode: Generic setting. Build Texture process with Mapping Mode: Orthophoto setting. Build Texture process with Mapping Mode: Adaptive orthophoto setting. Build Texture process with Mapping Mode: Spherical setting. To clarify, Mapping Mode is used to compose UV maps, and Blending mode is used to compose selected photo-images on created UV maps as textures.
The default blending mode is Mosaic. This setting selects the closest photo to corresponding surfaces and uses that image without blending with other overlapping photos. The Min Intensity setting is opposite of the Max Intensity setting; it uses minimum intensity images. The Disable setting is, again, for imported models that already have suitable textures Fig. Build Texture process with Blending Mode: Mosaic setting.
Build Texture process with Blending Mode: Average setting. A total of photos were taken and uploaded into PhotoScan. These photos overlapped each other well. Additionally, eight coded targets were placed on the model to aid in photo alignment; consequently, all photos were aligned successfully in PhotoScan. The next step was Build Dense Cloud. For this process, Quality: Medium, and Depth filtering: Mild settings were applied. However, the result was not successful because unnecessarily dense clouds were created between frames Fig.
To avoid the points formed between frames, Disabled depth filtering was applied; however, unnecessary points were still created. All photos were then reviewed, and it was noted that some obliquely taken photos displayed sides of frames as if there were timbers between frames Fig.
Subsequently, the author disabled photos that were taken diagonally hence reducing the total number of active photos to It must be noted that those disabled photos should not be deleted for the entire workflow process; all photos should be used in the Align Photos stage because maximizing the number of overlapped photos helps PhotoScan align photos.
Therefore, disabling photos, if required, should be done after photo-alignment is completed. Accordingly, Build Dense Cloud was applied with photos. The result of this process was drastically improved by disabling obliquely taken photos Fig. Dense Point Cloud of the saveiro wooden ship model.
Unintended points were created between frames. An example of photos of the saveiro wooden model that were disabled for Build Dense Cloud process. The photo was taken obliquely; therefore it displayed sides of frames as if there were surfaces between frames.
Dense Points Cloud of the saveiro wooden ship model after oblique angle photos were disabled processed with photos. Image: Yamafune After manually deleting unnecessary floating points, the Build Mesh process was applied. For this test, the author intended to use the saveiro model for two different purposes: 1 visualization purposes local coordinates system in Chapter II and 2 archaeological data purposes section profiles in Chapter III.
Therefore, two different sub-settings were chosen and applied on two duplicated dense cloud models. Thus the only difference between the processing of the two models was the Interpolation sub-setting. The Interpolation: Enabled sub-setting tends to fill holes on surfaces, yet it sometimes creates meshes among void spaces. The problem with the Interpolation: Disabled sub-setting is that created photogrammetric models tend to contain small floating meshes, or noisy dust-like surfaces.
This noisiness makes UV mapping difficult in the Build Texture stage. Consequently, the Interpolation: Enabled sub-setting was preferred for building high quality texture to support visualization. Before proceeding to the Build Texture process, both models were decimated simplified.
Each model had approximately 2,, faces; the excessive number of faces may compromise quality of textures because face count constrains spaces on UV maps; therefore, total model face counts should be decreased as much as feasible, while still maintaining the original shape of the source. Thus, both photogrammetric models were decimated from 2,, faces to , faces. Also, to acquire maximum quality textures, the previously disabled diagonally shot photos were re-enabled.
The result was satisfactory Fig. Finished Photogrammetric model of saveiro wooden ship model that is intended to be used for visualization purposes. Image: Yamafune Another photogrammetric model made for archaeological data purposes was first processed with the same sub-settings as the visualization purposes model, but the result was not good.
The poor texture was caused by noisy floating meshes because these tiny faces required individually separated spaces on UV maps. In other words, to acquire good archaeologically diagnostic textures on the created 3D model, meshes of the model must have a clean appearance. To solve this problem, the author deleted peripheral parts of the photogrammetric model. Finished Photogrammetric model of the saveiro wooden ship model that is intended for use as a source of archaeological data, or a reconstruction of the original shape of the frames.
In order to get better texture, peripheral meshes were removed. Image: Yamafune To summarize the above discussion, photogrammetric modeling in PhotoScan is not always successful on the first attempt. However, by disabling or enabling appropriate photos before each process, many problems can be addressed. Additionally, the results of processes differ based on selected sub-settings; therefore, the ultimate purpose of the model must be taken into account when creating a photogrammetric model.
In the following section, the author shall introduce other ways to solve problems of photogrammetric modeling by using exporting and importing tools in PhotoScan. Then, the modified models can be re-imported to PhotoScan to apply textures. For example, PhotoScan has great difficulty constructing meshes to model the sails of a ship.
The author copes with this difficulty by employing the exporting and importing features of PhotoScan. When the author applied Computer Vision Photogrammetry on a Chinese junk ship model, meshes cannot be created on the sail because it was too thin Fig. Similar failures of photogrammetric modeling often occur when subjects contain monotone faces. When this failure happens, PhotoScan cannot solve these problems automatically in the software.
However, using export mesh and import mesh commands, void spaces can be filled with desirable mesh from a different software Fig. However, it is important to maintain the models original spatial position; then, the model can be re-imported to PhotoScan at the exact same location as before it was exported. Photogrammetric model of a Chinese junk model with unsuccessfully reconstructed sail. Often sails are too thin to be reconstructed by photogrammetric software. Also, this can be done in other modeling softwares, for instance Rhinoceros 3D CAD modeling software.
Image: Yamafune As a result of this export and import void filling and mesh processing, the Build Texture process can now adequately process the original photos and new texture will be created on the edited meshes Fig. This technique is also useful when the created mesh has holes because of the monotone coloration problem. This technique may be useful when photogrammetric modeling of ships models or actual sailing ships is desired. The idea of video frame photogrammetry is to extract still frames from video footage and use those frame images as photos for Computer Vision Photogrammetry.
The basic Workflow of photogrammetric modeling in PhotoScan is the same; the difference is in the source and preparation of images. The author used two different techniques to extract still frames from video footage: selective extraction and automatic extraction. Automatic extraction is a way to automatically select still frames based on video frame quality and the position of the camera.
Since cameras shooting video do not control shutter speed, some still frames are subject to motion blur; therefore, selecting crisp still frames from video footage is important. Using this software, any still frame can be extracted and saved in Jpeg format with one click Fig. A major disadvantage of the selective extraction technique is its labor intensity because frames have to be manually selected. Using this method, still frames can be extracted automatically. The author used Adobe Photoshop CS6 for the automate extraction.
The Photoshop software series is typically employed as image editing software, yet it can also edit video files. Using Photoshop, lengths of video footage may be trimmed, and still images from video footage may be exported at a controlled frequency i.
This extraction is an automated process; thus, specific frames cannot be chosen for use in Computer Vision Photogrammetry. Consequently, some of the still frames automatically exported may be subject to motion blur. To avoid this failure, poor quality images have to be removed beforehand by using image quality estimation in PhotoScan.
After selecting all photos, the Estimate Image Quality command is shown by right clicking on selected images. This command estimates the sharpness of images and calculates crispness as quality.
When the process is completed, images may be re- organized based on quality by clicking on the Quality tab Fig. Then images that have quality below a certain threshold value should be removed. Unfortunately each photo must be opened and checked carefully, because the calculated value of quality and sharpness also depends on image size. As a starting threshold value, the author has found that rejecting images below a quality value of 0.
Employing this procedure allows photogrammetric modeling using video frames to proceed without poor quality images. Process for removing blurred images in PhotoScan. Image: Yamafune Video Frame Photogrammetry has both advantages and disadvantages. Two major advantages are wider coverage areas and better image overlapping. Since video recording does not require careful camera positioning, it tends to have much larger coverage areas than photo-shooting.
Another advantage is better image overlapping. Because one second of video footage is composed of 24 to 30 still images, the overlapping of consecutive images is marvelous. Balancing these advantages are the disadvantages of lower image resolution and fewer original colors.
This is because most high-definition HD resolution video footage only has x pixels per frame, while a photo from a DSLR camera with an ACL-S sensor contains approximately x pixels. Another disadvantage is less original color. As discussed in chapter II, auxiliary lighting can preserve the original colors of subjects; however, underwater environments require strong auxiliary lighting sources to penetrate water.
This strong lighting can easily be obtained by employing strobe lights while taking photographs; however, it is nearly impossible to apply strong lighting as effective as strobe flashes while video recording.
Even if a video camera has strobe lights that can provide auxiliary lighting, the power obtained from continuous spot-lighting is not strong enough in terms of original color retrieval. Consequently, photogrammetric models developed via Video Frame Photogrammetry tend to have a bluish-tone Also see Fig.
Photogrammetric models based on video footage have a much larger coverage area than photogrammetric models based on photos. However, without strong auxiliary lighting, created models tend to have bluish and greenish colored textures. The coverage area of photogrammetric models is limited because of the nature of photo-shooting.
However, created models tend to contain more detailed and diagnostic archaeological information due to their use of high-resolution images. Also, thanks to the use of strobe lights, created models can display more original colors. Nonetheless, the most important point is that whoever applies Video Frame Photogrammetry must understand both the advantages and disadvantages of using video footage as the source for photogrammetry.
Underwater Video Frame Photogrammetry Using GoPro As described above, Video Frame Photogrammetry is beneficial in many ways, especially for novice underwater photographers and divers who do not have knowledge of photogrammetry.
One reason is that it does not require complicated camera and strobe lights settings. Additionally, less expensive cameras with good High Definition HD underwater video recording capability are becoming commercially available.
Among these cheaper camera options, the most popular product today is likely the GoPro series. GoPro cameras have HD video recording systems that are very easy to handle in water, though their fish-eye lenses create distortion on captured images. Not only does Photoshop have image correction tools, it additionally has the processing ability to automatically extract still frames from video footage at a specified frequency.
Also, Photoshop allows color and haze correction over the entire video clip before still frames are extracted. In the end, satisfactory photogrammetric models can be created using GoPro cameras. The original GoPro video footage used for concept validation was taken by Charles Bendig, a graduate student at the University of West Florida. The videos were recorded by a GoPro Hero 3.
The total duration of the video was 2 minutes and 15 seconds and frame dimension was x pixels. In this workflow, Adobe Photoshop CS6 was used. Next, the video clips were trimmed to the desirable proportions to be used for Video Frame Photogrammetry. After these two filters were applied to the video clips, adjustment layers were applied to enhance the quality of the video clips; these adjustment layers were Curves, Levels, Color Balance, and Vibrance.
Curves adjustment enhances the contrast in an image while Levels adjustment enhances color-distributions of the image. Color Balance adjustment restores original colors by reducing greenish and bluish tones in the water, and the Vibrance adjustment enhances the natural colors of the subjects in the images Fig.
After satisfactory results were acquired with these corrections, still frames were extracted from video clips using the Render Video command. The original GoPro video footage before the color and distortion correction Image: Yamafune Figure However, as noted earlier, these frames were automatically extracted from the video clips in Photoshop and were not selected carefully with photogrammetry in mind.
Consequently, some images were subject to motion blur. Therefore, bad quality photos needed to be excluded before photogrammetric modeling could begin. Then, once the Align Photos process was started, all of the following photogrammetric processes were identical to the PhotoScans basic workflow for Computer Vision Photogrammetry. In addition to Photoshop there are many other open source software packages that can extract still frames from video footage, such as Free Video to JPG Converter.
However, to retrieve original colors and correct distortion, the use of Photoshop or a similar image editing or video editing software is recommended. Figure displays the photogrammetric model with the pre-processed image corrections in Photoshop and Figure shows one without any image corrections Fig.
Photogrammetric model based on video footage with color and distortion corrections applied in Photoshop. Orthophotos are high-resolution photomosaics, and PhotoScan can compose them based on calculated camera positions. Orthophotos are more accurate than traditional photomosaics because they do not have optical distortion. Moreover, when an orthophoto is created of a scale-constrained model, the exported orthophoto will also have correct proportions.
This image is also scale. GeoTIFF orthophotos are very useful, especially when archaeologists need to create a photomosaic for a larger area, such as an entire shipwreck site. When archaeologists have to create detailed photogrammetric models, the photo coverage area may be limited.
This situation can be caused by constrained diving time on the area, the number of photos needed to capture the entire area, or the limited computing power of available computers. The author suggests that when a photogrammetric model of the whole archaeological site needs to be created, it is easier to focus on small areas and piece the separated parts together later. When a model of a shipwreck site is created in separate batches, it is impossible to produce one complete orthophoto of the entire site from separated models though it may be possible if the project computer has substantial computing power and graphic cards to create one complete 3D model of larger coverage area, for instance m x 25m shipwreck sites , yet this is less likely on most archaeological projects because of budgetary constraints.
As briefly mentioned above, GeoTIFF orthophotos have georeferenced data; in other words, the image is a scale of the actual shipwreck site and images can open in correct scale and position in GIS software. Consequently, even if orthophotos of the archaeological site were created in separate areas or on different dates, they will merge automatically based on georeferenced information. First, the model must have georeferenced data, such as markers with XYZ coordinates. If it is selected, the exported TIFF image contains georeferenced information.
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