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Agisoft photoscan user manual professional edition version 1.2 free download
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Thanks for visiting the Crack. Auto calibration: frame incl. Multi camera projects support. Elaborate model editing for accurate results. Points classification to customize geometry reconstruction. LAS export to benefit from classical point data processing workflow.
KML file to be located on Google Earth. PhotoScan can process pictures in a wide range of digital image formats, but to facilitate alignment, ideally these images should conform to certain specifications. Since points have to be detected in each picture, the images should preferably also be as sharp as possible high aperture , be well-lit and contain as little noise as possible low ISO. Similarly, the use of flash photography is not recommended since this will cause strong shadow differences across images, which might confuse feature detection.
Figure Recommended camera positions for various photogrammetry recording scenarios. Since Agisoft is a commercial enterprise, the company is somewhat secretive about the specific algorithms used for each of these processing steps.
Nevertheless, some information can be surmised from the online discussion forums and from scientific articles. Today most digital pictures contain.
The specific algorithm used for the subsequent camera alignment step is unknown, but it involves the calculation of approximate camera locations which are then refined using a bundle-adjustment technique Semyonov, Nevertheless, Remondino et al. These patches are then projected onto their corresponding points on the surface mesh, and source photos subsequently blended to create the texture atlas Semyonov, Once again little is known about the origin of the actual algorithm used.
For each of these four steps different processing settings can be chosen in order to fine- tune the processing procedure to the needs of the specific image sequence. Additionally, between each major processing step the user has the opportunity to perform additional smaller actions in order to improve the final results. These actions include picture masking, deleting erroneous points, importing camera positions from external files, setting the reconstruction bounding box, and so forth.
Alternatively the 3D models can be uploaded to the online platforms Sketchfab and Verold. Two Editions, Four Price Classes PhotoScan is available in two editions: a Standard Edition targeted at hobby users, and a Professional Edition targeted at survey professionals and the digital animation industry.
While both Editions contain all the essential features discussed above, the Professional Edition has additional features such as model scaling, marker-based chunk or picture alignment, geo-referencing based on known ground coordinates and the possibility of 4D recording i.
There is no time restriction on any of these licenses and the software can be freely updated to more recent versions. Additionally, anyone wishing to try the software for the first time has access to a day free trial, which offers exactly the same functionality as PhotoScan Professional Edition. Applications Today an immense selection of different camera models, lenses and accessories are available to meet a wide range of imaging requirements.
Multi-spectral imagery opens up new perspectives such as feature-detection through foliage. In tight spaces a wide-angle lens might be more appropriate, in order to record a lot of details from a close range in a limited number of pictures.
If a subject has to be recorded through a glass casing, a polarising filter can be used to avoid reflections. In short, the possibilities are endless. These applications range from artistic modelling projects to face, full-body and prop scanning for game design and the film industry, to aerial surveys in the context of mining activities, agricultural and environmental management or city planning.
Nevertheless, the question remains how well PhotoScan can cope with the specific challenges faced in the underwater environment. This issue will be explored in the following chapter, based on the lessons learnt from working with PhotoScan to record three archaeological shipwreck sites. The data in question covers three shipwreck sites, one in Denmark and two in the Netherlands, all dated between the late 16th and early 18th century.
All data was processed on a relatively high-end laptop equipped with an Intel Core iMQ 2. Photogrammetry using Casual Video Footage The first case study concerns photogrammetry modelling using data captured in the summer of , during the excavation of the Lundeborg Tile Wreck by the Maritime Archaeology Programme of the University of Southern Denmark. Today the site, which consists of the remains the location of the Lundeborg wreck.
Within this zone, three areas of special interest were excavated during the campaign, namely: 1. Since photogrammetry did not form part of the original Lundeborg project design, these areas were originally recorded using manual offset drawings and tape measure trilateration Figure Nonetheless, the site archive also contained several hundred pictures and a couple of videos, captured for redundancy purposes.
The areas excavated and recorded in are outlined in blue. Main Challenges In theory the Lundeborg wreck made an ideal scenario for underwater photogrammetry: the site is relatively flat, so its geometry is not too complex, underwater visibility was generally good, and since the site is located at just 5 m depth, there was an abundance of natural light.
As such, typical issues of underwater light absorption or limited visibility were of little concern during the Lundeborg recording. In this case the main challenge was that the footage had been captured without photogrammetry in mind. Even if the hundreds of pictures combined might have covered every part of the wreck, the pictures were taken over a series of days.
Over the course of these days the excavation progressed and as such vegetation and sediment were gradually removed, baselines and timber tags were added, etc.
In other words, the site looked very different from day to day and as a result PhotoScan was unable to detect enough matching features across pictures taken on different days. During video recording the diver was continuously moving around the wreck, and as a result — even though most of the videos were just a couple of minutes long — each video contained a lot of image data about the site. Nevertheless the video footage also had certain drawbacks.
First of all, like in aerial photogrammetry, during underwater photogrammetric recording it is generally recommended to record sites in a lawnmower pattern in order to ensure sufficient image overlap. However, since these videos had not been captured for photogrammetry, the prescribed lawnmower survey pattern had not been followed.
Secondly, the videos were recorded using a GoPro a camera model equipped with a fisheye lens. This lens type causes extreme perspective distortion and is therefore generally best avoided for photogrammetry purposes. Finally, frames extracted from video footage do not contain the camera. EXIF metadata which can help PhotoScan correctly calibrate cameras, and as such photogrammetry modelling from videos is not usually recommended.
Processing Procedure Three videos — one for each of the areas excavated in the campaign — were identified as best candidates for photogrammetry. Total recording time for all three videos was about 6 minutes. In order to provide sufficient overlap between consecutive frames, a frame was extracted every couple of seconds. From these initial sparse point clouds a dense point cloud, mesh and textured mesh were subsequently generated. The processing of all three datasets — from extracting the frames to producing the textured models — was done in a single afternoon.
The results are shown in Figures Textured 3D model produced in PhotoScan. Figure Top view of the timbers to the southeast of the Lundeborg wreck mound excavation area 3. Discussion Since we can only compare the photogrammetry models to the manually drawn site plan which has its own inherent inaccuracies , it is hard to draw any conclusions regarding their absolute accuracy.
Nonetheless, simple visual inspection suggests that the accuracy is certainly high enough for archaeological purposes. This has profound methodological implications: whereas we spent several days recording each excavated area by hand using offset measurements, video recording of each area took only a couple of minutes.
Furthermore, the 3D textured models contain a lot more information than the 2D site plan, and since the photogrammetry process is largely automated, the 3D models are more objective and less prone to human errors than traditional offset drawings.
For instance, we now know that during the original campaign we forgot to record at least one timber in excavation area 3; while the timber is visible in the photogrammetry model, it is missing from the site plan. From a photogrammetric point of view, these experiments have demonstrated that 3D modelling using frames extracted from videos is a viable alternative to 3D modelling from picture sequences.
Furthermore it succeeded in processing such casual footage not just once, but for all three videos, allowing us to produce a photogrammetric model of each of the areas excavated during the campaign. That being said, in the current case study the photogrammetry models cannot serve as a replacement for the Lundeborg site plan, simply because the videos were captured from too great a distance, and when parts of the site were still covered by sand.
The result is that while the overall geometry of the site is clearly modelled, small details such as nail holes or tool marks are not always visible. Photogrammetry using Legacy Data The second case study concerns photogrammetry modelling using data captured during the historic Aanloop Molengat excavation. The Aanloop Molengat site is located in the North Sea, to the west of the island of Texel, at a depth of 16 m. Extending over an area of 33 by 13 m, the site consists of a 17th century Dutch vessel built in the Dutch-flush tradition, carrying a significant cargo of raw and half-finished products.
Following underwater visibility of up to 10 m during the original assessment of the site in , Digital Photogrammetry was chosen as primary recording method for the project.
In order to help ensure image overlap, initial photogrammetry experiments in and made use of a rigid frame structure allowing archaeologists to take pictures at fixed intervals across the site. Figure Site plan of the Aanloop Molengat wreck. Unfortunately, despite using a systematic data capture strategy, and despite partnering with photogrammetry experts at the Delft University of Technology as well as with an engineering firm specialised in geodetic surveys, both the initial photogrammetry trials in and , and the renewed attempts in , failed Vos, In particular, we will make use of the stereo pictures from Main Challenges Unlike the Lundeborg case study, the stereo pictures from were taken for the explicit purpose of photogrammetry modelling.
Nevertheless, the data proved extremely challenging to work with, due to both the quality of the original picture sequence, and how these pictures were later digitised. Original Picture Sequence The most positive aspect of the picture sequence was the fact that the images were captured in a very systematic manner.
The mobile, diver-based photogrammetry system adopted in consisted of a stereo-camera rig using two pre-calibrated Hasselblad MC Ocean Optics cameras. In order to ensure image overlap the cameras were mounted 50 cm apart and white survey lines were laid out across the wreck at 1 m intervals. The surveyor then followed these lines in a lawnmower pattern, taking pictures at a steady distance of approximately 1.
Easily recognisable 75 cm long scale bars were spread out more or less evenly across the site to provide scale. Unfortunately there were also various issues with the picture sequence.
In visibility on the Aanloop Molengat wreck was much worse than it had been during the original site assessment in , and as a result the pictures were not very sharp and did not contain a lot of contrast. Furthermore objects closer to the camera were often overexposed, while objects further away remained shrouded in shadows. Finally a significant portion of the photos had a strong vignette or strange lens flare which completely obscured part of the image Figure Besides poor image quality, there were also issues with the image overlap.
Next, while overlap between consecutive lanes across the wreck was generally adequate, in some cases one lane was recorded prior to clearing the wreck of sediment, while the adjoining lane was recorded after the sediment was removed. The result is that in such cases the pictures in the two consecutive lanes looked completely different, making it impossible for PhotoScan to automatically detect matching features.
Figure Two sample pictures from the Aanloop Molengat dataset. The images are blurry and contain areas with excessive shadows. Additionally the image on the left suffers from strange lens flares, and the image on the right is heavily overexposed. It is clear that PhotoScan will have difficulty detecting feature points in such pictures.
Digitised Picture Sequence Rather than working with the original stereo photographs, in this case I had to work with digitised scans.
The main advantage of this data was that it was well-catalogued: each of the 24 lanes across the wreck was indicated by a number from to , and pictures within each lane were grouped in stereo-pairs — between 5 and 12 pairs per lane.
However, the way in which the photographs were digitised again presented significant challenges to the photogrammetry workflow. Firstly, the slides were simply not scanned very consistently: the centre point and orientation of each image were indicated by fiducial markers in the original slides, but these fiducial markers were not aligned in the same position in each scan.
However, if PhotoScan is to correctly calibrate each image, it is important that the focal points and projection rays of different images match. Secondly, each scan was cropped to a different aspect ratio, often by cropping off the edges of the image, meaning information was lost along the borders of certain pictures. The fact that images were cropped to different aspect ratios also meant that PhotoScan considered them to be from different cameras, which further complicated camera calibration.
Lastly, certain slides were scanned while still in a glass casing. In some cases annotations had been made on this glass casing with a marker, thereby obscuring the original image underneath and impeding feature detection. Processing Procedure As a result of the issues described above, initial photo alignment failed: using the digitised scans in their original format, only a handful of the images aligned successfully in PhotoScan.
A major breakthrough was the realisation that images could align a lot more easily after first being pre-processed in picture editing software.
Picture Pre-processing Image pre-processing was done in Adobe Photoshop. Rather than editing images one by one, pictures that required similar adjustments were grouped into folders and then batch-processed. After processing, the resulting pictures contained less extreme shadows and highlights and were a lot crisper than the original images, thereby making it easier for PhotoScan to detect matching feature points Figure Additionally, due to the resizing of the image canvases, all pictures were now the same size and aspect ratio, meaning PhotoScan could recognise them as a single camera group and therefore auto-calibrate them much more accurately.
Figure Original image left compared to enhanced image right. The enhanced image is much sharper, more balanced and shows more detail, particularly in areas which initially suffered from excessive lighting or shadows. This time many more pictures aligned correctly, although certainly still not all of them.
The main issue were the consecutive lanes where one lane had been cleaned prior to recording and the other had not; in these cases PhotoScan was simply unable to detect matching features across both lanes. To overcome this problem, the pictures were divided into four different chunks, each chunk containing several consecutive lanes across the wreck.
These chunks were then processed separately, and as a result the vast majority of pictures within each chunk aligned correctly, though still not all of them.
This approach is similar to the image alignment procedure traditionally used in Digital Photogrammetry procedures. After aligning the remaining pictures within the four chunks in this manner, a dense point cloud, mesh and textured mesh were subsequently generated for each of the chunks. Finally the aligned chunks were merged, a new texture was created for the site as a whole, and the model was scaled to its real-world dimensions based on the length of the 75 cm-long scale bars spread across the wreck.
The result is a single coherent, scaled and textured 3D model of the Aanloop Molengat site Figure 28, for high resolution image see Annex 1 , created 24 years after the original photogrammetry survey.
Discussion According to the error margin calculated by PhotoScan, the model is accurate to within This error margin is determined based on the disparity between the calculated position and actual positions of markers, and on the disparity between the lengths of the different scale bars identified in the model. However, it is important to stress that this method of error estimation can only account for local linear deformations within each of the four chunks.
Nonetheless, when the model is compared to the Aanloop Molengat site plan, visual inspection at least demonstrates that the two are very similar. Figure Top view of the Aanloop Molengat site. The most important lesson learnt was that image pre-processing can significantly improve alignment results. Nonetheless, even after pre-processing the pictures in Photoshop, a lot of time went into processing the pictures in separate chunks in PhotoScan, manually aligning pictures that had failed to align, then manually aligning chunks and finally reprocessing the overall model to create a single coherent result.
In practice, full modelling of the site took about three weeks. However, most of this time was spent testing a large number of different approaches before finally finding a sequence of processing steps that produced adequate results. As such, given what I know today, it would probably only take about three days to reproduce the same 3D results.
Certain site features, such as several of the canon which are visible in the site plan, are not visible in the 3D model, simply because they had been removed prior to recording. Other areas, such as the keel which protrudes beneath the cargo at the east end of the wreck, were not recorded in the surveys and are therefore likewise missing from the final model.
However the photogrammetry results could still be useful in order to add detail to the current site plan, and to control its overall accuracy. Furthermore the photogrammetry model has a number of applications which the 2D site plan does not.
Photogrammetry in a Low-Visibility Environment In the Lundeborg and Aanloop Molengat case studies we processed picture and video sequences previously recorded by other archaeologists. In order to really experience the dynamics of using photogrammetry in the field, in a third and last case study I therefore wanted to participate in the photogrammetric recording of a wreck myself.
Organised by the underwater division of the Cultural Heritage Agency of the Netherlands, over the course of the project several wrecks were surveyed and recorded. Among these, this case study will focus on one site in particular: the Oostvoornse Meer 8. The wreck, which is located between 16 and 20 m depth Figure Map of the Netherlands and covers an area of approximately 15 by 8 m, consists indicating the location of the of the remains of a Dutch merchant vessel, again built in Oostvoornse Meer.
Dendrochronological dating of the ship timbers indicate the vessel was built in the second half of the 17th century, and objects found on board suggest the ship sank in the first quarter of the 18 th century. A cargo of Spanish or Portuguese jars containing olive pits implies that the vessel was probably on its way back from the Mediterranean when it sank. Today the Oostvoornse Meer is a brackish water lake, but in the past this body of water was an estuary leading from the North Sea, up the Brielse Maas to the city of Rotterdam.
Some clarifications have been added to the original site plan by the author. Finally the project also served as a field school for maritime archaeology students. As such, for one week we, the students, were put in charge of the campaign. Given the promising results obtained in the previous case studies, and after preliminary photogrammetry experiments on the Oostvoornse Meer 14 wreck, we therefore chose photogrammetry as the primary recording method for the Straatvaarder survey.
As a backup, the site was also recorded using offset drawings. Based on this prior experience, these issues were easily avoided during the Straatvaarder recording. Nevertheless, the site presented its own set of challenges. The second and most important challenge was visibility — the complications of working with photogrammetry in low visibility have been discussed in Chapter III.
In this case recording conditions varied considerably throughout the campaign: on day 1 visibility and natural lighting were good, but on days 2 to 5 the water turned murky and visibility was often no more than a metre, regularly falling below 50 cm.
Recording Procedure In order to ensure sufficient image overlap in such low-visibility conditions, rather than using regular picture sequences, in this case photogrammetric modelling was done based on video footage. In particular, given the promising results obtained with a fisheye lens on the Lundeborg wreck, a GoPro HERO3 Black edition camera was used to capture the necessary data.
During the first day of recording underwater visibility was surprisingly good, and enough natural light reached the wreck to be able to film without artificial lighting. However, from day 2 onwards the visibility dropped drastically and as such we had to record with an artificial light source.
Initially a single diffuse light, mounted right underneath the GoPro camera was used; however this caused issues of severe backscatter. Eventually we therefore mounted the GoPro ca. Directly prior to filming with LCD screen — in order to ensure that the remains were entirely clean — divers again made a brief pass over the area, wafting away any remaining sediment. During recording, an LCD screen mounted on the back of the GoPro allowed the divers to assess the data on the go, diffuse light in order to ensure that each video was clear enough for photogrammetry purposes.
To maintain a logistical overview of the data, each pass over the wreck was recorded as a separate video file. The upstanding stempost required a slightly different approach; in this case a diver Figure Simple camera setup swam in a slow spiral pattern around the post, recording used for recording.
Once back at the surface, the diver communicated how far he had gotten during his dive, and the next diver was sent down with another GoPro, equipped with a fresh battery and memory card, to pick up recording where the last diver left off. The data on the first GoPro was then transferred to the on-site laptop and the videos were catalogued according to date, diver and the part of the wreck recorded.
Over the course of four days, during 15 individual dives, short videos were recorded, for a total of hours of video footage. The average length of each video was little over a minute, and typically during each 30 minute-dive about 11 minutes of video footage could be recorded.
In the end ca. The remainder was either of insufficient quality for photogrammetry purposes, or redundant footage covering areas of the wreck already captured in other videos. Processing Procedure Photogrammetry processing using the on-site laptop began as soon as the GoPros were back at the surface. The interval at which frames were extracted depended on the underwater visibility of the video; at lower visibility more frames had to be extracted per second, while at higher visibility less frames also provided sufficient image-overlap.
On average about 2 frames per second were extracted. By leaving the software to run for the remainder of the day and throughout the night, by the next morning we usually knew whether all images in the chunk had aligned correctly. If not, alignment could be repeated using different settings until an adequate result was obtained or — in the worst-case scenario — the survey of a particular area could be repeated in order to obtain better footage.
Eventually a total of video frames of which aligned successfully , were processed in 11 separate chunks, each chunk containing anywhere between and images. Finally the chunks were merged into a single coherent model, a new texture map was generated for the site as a whole, and the model was scaled using known distances measured on the site. The result is a complete, scaled and textured 3D model of the Straatvaarder wreck Figure 32, for high resolution image see Annex 2.
Data Elaboration Since photogrammetry was the primary recording method on the Straatvaarder survey, the 3D data had to be further processed in order to create two-dimensional content for the publication.
This included a site plan, enhanced mesh, cross-sections and elevation models of the site. Perhaps the most important result of any excavation is a coherent site plan showing the various elements of the site and the relationships between these elements.
Although a textured 3D model or orthophoto produced using photogrammetry might contain more texture detail than a simple lines drawing, research has shown that lines drawings are easier to understand at a glance and are better at conveying site interpretation Gilboa et al.
Figure Top view of the Straatvaarder wreck. Enhanced mesh produced in Meshlab. Figure Site plan of the Straatvaarder wreck produced in Illustrator. In order to produce the site plan of the Straatvaarder wreck, a two-dimensional orthophoto of the site, based on the textured 3D model, was first exported from PhotoScan.
This orthophoto measures by pixels, which is roughly equivalent to taking a single metrically correct image of the site using a megapixel camera; as such it contains a lot of texture detail. After exporting, the orthophoto was further edited in Photoshop in order to increase contrast and reduce haze so that small details stood out even more Annex 2.
Whereas orthophotos are great at showing texture details, other image formats are better at showing geometric details. A two dimensional rendering of this enhanced mesh was then saved Figure In order to draw up the site plan, both the Photoshop-enhanced orthophoto showing the texture details, Figure 32 and the Radiance Scaling-enhanced mesh image showing the geometric details, Figure 33 were imported into Adobe Illustrator. The files were placed on different layers and positioned so they overlapped precisely.
Figure 34 shows the resulting site plan. The same general workflow was also used to produce a lengthwise cross-section of the wreck Figure It remains difficult to display three- dimensional information in two dimensions.
Nevertheless, in order to illustrate the height differences present throughout the wreck site, a colour-coded Digital Elevation Model of the site was also produced in Figure Digital Elevation Model of the Straatvaarder wreck QuantumGIS Figure The deepest parts of the site are shown in blue, the higher parts in red. Discussion The main complication during the Straatvaarder survey was working with the challenges presented by the low visibility environment.
Recording in low visibility meant having to record very close to the wreck in order to get sharp imagery: average recording distance from the wreck was 66 cm, though in particularly poor visibility this could drop to as little as 20 cm.
This in turn meant a lot of close-up video frames had to be aligned in order to provide full photogrammetric site coverage. Accordingly, as the number of images in a dataset increases, the number of comparisons between images increases exponentially.
Mathematically the formula is as follows: In a dataset with n images, PhotoScan will perform comparisons between images. Consequently, whereas a dataset of images results in about thousand image comparisons and would require perhaps two hours to process using our computer setup, a dataset of images would require nearly As a result, in order for us to be able to assess the photogrammetry results while still in the field, we had to keep the processing times — and therefore the number of images to be processed — to a minimum.
However, thanks to computer vision algorithms, the procedure is now largely automated and therefore relatively fast: the models of the 11 individual chunks were created over the course of five days during the campaign itself, and subsequent tidying up of the results, chunk alignment and final model generation required another two days, after the campaign had ended.
In addition to having to record close to the wreck, limited visibility also forced us to record with artificial lighting on days 2 to 5, whereas we had recorded without artificial lighting on the first day, when visibility was good. This is not really an issue for archaeological purposes, since the colours do not influence site interpretation. In the future it would therefore be better to record the entire site using the same light configuration. As I have mentioned above, these three divers only needed a total of 15 dives, or a combined hours of dive time, to record the entire wreck site.
This means that — had we devoted all of our recording time exclusively to photogrammetry — several divers working simultaneously could have easily recorded the entire site in a single afternoon. By comparison, in order to create a backup plan of the site, over the course of four days at any given time up to three people were simultaneously recording the site using manual offset drawings.
It is therefore clear that despite the limited on-site visibility, during on-site recording photogrammetry was significantly more efficient than manual recording methods.
In terms of accuracy, based on known measurements performed on the site, PhotoScan calculates that the error margin within individual chunks is about 4 mm; again much better than what could be expected from manual recording methods.
As a result treenails, nail holes and in many cases even the grain of the wood, are clearly visible. As such it is important to stress that photogrammetry models are not just a nice tool for public dissemination — they also provide a highly accurate record of the site for scientific interpretation.
In fact, the amount of information and detail contained in the final textured 3D model far surpasses anything we could have hoped to achieve using manual recording methods. That being said, despite recording close to the wreck remains, and despite wafting away all sediment prior to recording, in the final orthophoto and enhanced-mesh of the site, a number of important construction details were still not visible. This could involve first recording the wreck using photogrammetry, and then sending a diver down with a lines drawing of the photogrammetry results in order manually fill in any possible missing details.
The Straatvaarder survey also proved very useful in order to draw some interesting conclusions regarding the actual dynamics of working with photogrammetry in the field. A first observation was that, given some basic pointers, all participants quickly got the hang of recording footage with the necessary image quality and overlap for photogrammetry purposes.
As such photogrammetry is clearly an accessible recording method to learn, even for people who are unfamiliar with the complex science and principles behind the technique. Secondly, photogrammetry recording using video footage proved to be an effective way of ensuring sufficient image overlap in low visibility conditions. In these conditions taking a picture every couple of centimetres to ensure overlap would have been very challenging, and as such video footage provided an easy alternative.
Divers could simply film in straight lines, and only had to worry about ensuring enough overlap between consecutive lanes across the wreck, rather than having to worry about image overlap within each of these lanes. In this regard, having an LCD screen mounted to the back of the GoPros also proved very helpful in order for participants to ensure that the footage they were capturing was clear enough for photogrammetry purposes.
Lastly, having a powerful field computer turned out to be invaluable. By appraising video footage right after it was recorded, and immediately processing the resulting image frames, all results could be assessed on location and — if necessary — footage that produced inadequate models could be re-captured. This helped us ensure good coverage for every part of the site, in order to produce a coherent overall site model.
The conference poster is included in Annex 3. Despite having to deal with the various issues of using photogrammetry in the underwater environment, throughout these case studies PhotoScan proved capable of consistently producing excellent 3D results from difficult input data. Furthermore it is important to note that all results were produced not by an engineer or photogrammetry specialist, but by an archaeology student, using only low-cost widely available hardware and software.
When we consider our case studies, a first observation is therefore that Computer Vision Photogrammetry was able to reproduce the same positive achievements published by other maritime archaeologists in the past see Chapter III : compared to manual recording methods, photogrammetry was again capable of reducing underwater recording times and producing more accurate, detailed and objective three-dimensional results.
In terms of underwater recording times, in the Lundeborg and Straatvaarder case studies photogrammetry recording proved drastically more time-efficient than manual recording methods.
Whereas a single diver recorded the Lundeborg excavations in ca. Similarly, despite the limited visibility on the Straatvaarder wreck, three divers were able to record the entire wreck in just hours of dive time, whereas over fifteen divers alternated to record the wreck manually over the course of five days.
In terms of accuracy, simple visual inspection of the photogrammetry results, as well as comparisons with past site plans, at least suggests that the overall accuracy of the PhotoScan results is very high. On land this is typically done using highly GPS coordinates. Underwater, accuracy control is a notoriously difficult issue to tackle, but in the future this could be achieved by for instance establishing a network of ground control coordinates using tape measure trilateration software such as Site Recorder.
In terms of detail, the level of detail attainable with modern photogrammetry is perhaps best illustrated by the high resolution orthophotos of the Aanloop Molengat and Straatvaarder wrecks, contained in Annex 1 and 2. Generally timber numbers, treenails, nail holes and in some cases even the grain of wood can be discerned; this amount of detail far surpasses anything we could realistically hope to achieve with manual recording methods.
The lines drawing produced in the Straatvaarder case study demonstrates how this detailed texture and geometric information can be combined in order to create a highly detailed site plan of a wreck. Since the textured photogrammetry models document every bit of pixel detail visible in the original picture data, they are also more objective than manual recording methods, which record only those features considered important by the researcher at the time of recording. Furthermore, being largely automated, photogrammetry was less prone to human errors.
By comparing the site plans to the photogrammetry results, in our case studies we observed that on at least two occasions errors had been made during the original manual recording of the site: on the Lundeborg wreck one plank was simply omitted from the site plan, and on the Straatvaarder wreck the orientation of one of the timbers had accidentally been mirrored.
For each of the presented case studies the three-dimensional results provided certain advantages over simple 2D data. For instance, they could be used to determine the mass of various elements of cargo, or to help make a three-dimensional reconstruction of the vessel in CAD software.
In short, the present case studies further confirm the positive qualities of photogrammetry recording previously established by other maritime archaeologists. However, in Chapter III we also came to the conclusion that in the past photogrammetry had suffered several important limitations: the recording method had been too expensive, too technical and too unreliable for most archaeological projects. Based on the case studies examined in this thesis, I think it is fair to say that these past obstacles have now been successfully overcome.
Nevertheless this information is essential in order to produce good photogrammetry results. In the past researchers have resolved this issue by using specially pre-calibrated cameras or corrective lenses, by calculating the cumulative effect of different refractive indexes, or by taking pictures of an underwater calibration grid in order to determine camera calibration in post-processing.
Needless to say this was a very technical, time-consuming procedure. Today, using feature detection algorithms and feature-based alignment, PhotoScan can automatically determine the position and orientation of thousands of images within a matter of hours.
Once again this was a very time-consuming process, which resulted in a model containing only the relatively small number of points and features manually identified by the researcher.
Using feature-detection algorithms, PhotoScan can now plot millions of points automatically within a matter of minutes. By subsequently generating a textured mesh of the resulting point cloud, the final model is a complete 3D copy of the actual site, rather than just a selection of points and features.
In conclusion, as a result of the large degree of automation provided by computer vision algorithms, photogrammetry is now easier to use and faster than ever before, while at the same time producing more complete results than those attainable at any time in the past. Next, because photogrammetry is now easier to use, archaeologists no longer have to rely on specialists to process their data.
The overall result is that today photogrammetry is much more affordable and accessible. Finally, because photogrammetry processing times have now been drastically reduced, the photogrammetry data can be processed and assessed while still in the field. As illustrated in the Straatvaarder survey, this is very important in order to ensure that each part of the archaeological site is adequately recorded and successfully modelled before the campaign draws to a close. As a result, compared to past photogrammetry approaches Computer Vision Photogrammetry is now also a lot more reliable.
The Significance of Photogrammetry in our Discipline over Time In Chapter II we saw how each new development in photogrammetry gradually contributed to more powerful, easier to use workflows and reduced costs.
However what has the true impact of these developments been on the discipline of underwater archaeology as a whole? Similarly, what might the impact of current developments in Computer Vision Photogrammetry be? After assessing several publication databases, Google Scholar turned out to provide the most complete record.
Removing patents and citations from the search results, I then noted the number of publications matching these criteria over five year intervals. The results are shown in Figure Naturally this approach suffers certain limitations, such as the fact that it ignores the significant contributions made by Scandinavian, German, French, Spanish, etc.
We can observe that after the publication of the impressive results obtained at Yassi Ada, interest in photogrammetry surged massively in the s. However, in the years following this initial optimism, the method gradually received less and less attention, reaching an all-time low in the early s. Over the past five years, with the advent of Computer Vision Photogrammetry workflows, this number has nearly doubled. Nevertheless, until recently the Computer Vision Photogrammetry approaches used in underwater archaeology still had several important limitations.
The available software packages were difficult to use, and the results from several different software applications had to be combined in order to produce the final photogrammetry model see Chapter IV. Furthermore it is clear that many other researchers are rapidly reaching the same conclusion: in the months since I started working on this dissertation, various articles have been published which similarly use PhotoScan for underwater archaeological site recording Zhukovsky et al.
Meanwhile, many other underwater archaeologists have started sharing their PhotoScan results through online user groups and forums. As a result underwater 3D recording is now accessible to anyone, at a fraction of the cost of other techniques such as high-frequency sonar.
Compared to manual recording methods, Computer Vision Photogrammetry is capable of significantly reducing underwater recording times, while simultaneously producing more accurate, more detailed and more objective results. The overall outcome is a more complete and more scientific record of the archaeological site, allowing for a more informed interpretation of the archaeological remains in the present, as well as a more comprehensive reassessment and scrutiny of the results in the future.
In fact, because the method is now so affordable, and because it can drastically reduce underwater recording times thereby cutting the expenses of running a lengthy fieldwork campaign , for most underwater archaeology projects the cost of using photogrammetry will now actually be lower than the cost of using manual recording methods. In conclusion, given the highly promising results obtained in these case studies — as well as a growing number of other case studies published both in the scientific literature and online — we have every reason to believe that Computer Vision Photogrammetry will play an increasingly prominent role in the field of underwater archaeology in the years to come.
These include questions related to how photogrammetry might influence and hopefully improve the way we conduct research, how we can best preserve this vast amount of digital data for future generations and how this new technology could be used to inform and engage the sport diving community, as well as the general public.
Whatever the focus of such research, it is clear that many exciting future prospects still lie ahead. Thesetoframes be processed to a minimum were processed in the current case study discusses the use of this technique to sufficient image overlap for modelled. The model was of video frames ca. Finally the model of the wreck plan and profile drawings video frames into several chunks visibility conditions. The site is located elevation model of the wreck. PhotoScan recorded the entire site in a single afternoon visibility variable: often no more All results were produced using widely available low-cost site extent depth than 1 m, regularly dropping cameras and affordable, easy to use software.
The case study ca. In this article, I share my experience in working with one such software package, namely PhotoScan, to record a Dutch shipwreck site. Based on the results of this case study, Computer Vision Photogrammetry compares very favourably to manual recording methods both in recording efficiency, and in the quality of the final results. In a final section, the significance of Computer Vision Photogrammetry is then assessed from a historical perspective, by placing the current research in the wider context of about half a century of successful use of Analytical and later Digital photogrammetry in the field of underwater archaeology.
I conclude that while photogrammetry has been used in our discipline for several decades now, for various reasons the method was only ever used by a relatively small percentage of projects. Recording is the most important step in the On the other end of the spectrum researchers can choose from a whole process. It is only through the accurate recording of a site that project. Proper recording ensures the preservation of knowledge for future Fortunately, in recent years one recording method has emerged generations and forms the groundwork for any research that which promises to bridge the gap between these two extremes, might follow.
As such it is crucial that archaeologists strive to providing highly accurate three-dimensional data of underwater document each site to the best of their abilities; as accurately, sites, at a fraction of the cost of more advanced techniques. This completely and objectively as possible within the time, budget method, which can be referred to as Computer Vision and environmental constraints imposed. On the one hand Photogrammetry, essentially allows users to upload a series of recording sites to the best of our abilities means making the overlapping pictures of a scene or object into dedicated most of established recording methods.
On the other hand, it software in order to semi- automatically generate a 3D model also entails reassessing these methods and exploring new, of that scene or object. Having initially gathered pace in innovative and perhaps better ways of documenting our terrestrial heritage research, over the past few years Computer heritage.
Vision Photogrammetry has also been increasingly used for underwater recording Mahiddine et al. These methods are robust enough to be used in the underwater environment, and they have the In this article I want to contribute to this growing body of advantage that they are both affordable and reliable.
On the research by sharing my experiences in working with one other hand they are not very accurate, and — with the exception Computer Vision Photogrammetry software in particular, of photography — they are very time-consuming and prone to namely PhotoScan, to record and model a late 17th — early 18th human errors. Furthermore, these recording techniques century Dutch shipwreck.
Based on this case study I will This contribution has been peer-reviewed. Within this much 2. If a feature point is detected in based in St. Once all of these additional feature points are added to survey professionals and the digital animation industry.
While the existing sparse point cloud, the result is a much more both editions contain all the essential features required to detailed dense point cloud Semyonov, Anyone wishing to try the software for the first time can also download a free While various other photogrammetry applications follow a very day trial of PhotoScan which offers the same functionality as similar workflow, after initial experiments with a number of the Professional edition.
First of all, PhotoScan was simply the most reliable PhotoScan converts images into textured 3D models in four program I tested: whereas large image sequences or images of straightforward processing steps, namely 1 Align Photos, 2 geometrically complex objects often failed to align or aligned Build Dense Cloud, 3 Build Mesh and 4 Build Texture.
For incorrectly in other software applications, they generally aligned each of these steps different processing settings can be chosen without much trouble in PhotoScan. Secondly, a lot of in order to fine-tune the processing procedure to the needs of applications only perform part of the photogrammetry workflow the specific image sequence. Additionally, between each major described above, meaning users have to work with one program processing step the user has the opportunity to perform to perform initial picture alignment and then use another additional smaller actions in order to improve the final results.
These actions include picture masking, deleting erroneous By contrast, PhotoScan conveniently combines all processing points, importing camera positions from external files, setting steps from camera calibration to textured mesh generation in a the reconstruction bounding box, and so forth.
Since all of these single software package. As mentioned above, the software follows a very overlapping pictures. Feature-based alignment which compare various photogrammetry software applications builds on the principle of intersecting rays; essentially, using at to one another Remondino et al.
As such, while other programs might cost slightly less, in projected from the focal point of each picture, through the my experience PhotoScan is well worth the investment in order detected feature points. The place where these rays intersect to avoid the hurdles and frustrations of working with cheaper, then determines the 3D coordinate of that feature point.
The less powerful software packages. When the process This contribution has been peer-reviewed. Structure-from-Motion modern photogrammetry is capable of, by reconstructing a Photogrammetry makes reference to a specific method relatively large, complex three-dimensional site based on commonly used to automatically generate a 3D point cloud imagery captured in low-visibility conditions using widely from images, but the name leaves no room for other, often available, low-cost cameras.
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Whatever the focus of such research, it is clear that many exciting future prospects still lie ahead. Two consecutive photos, functioning as a stereo-pair, then had to be oriented correctly on the stereoplotter: this involved a interior orientation, meaning the pictures had to be oriented to compensate for their respective camera directions and focal lengths b relative orientation, meaning both pictures were aligned to overlap and c absolute orientation, meaning the pictures were positioned on a map in reference to known ground control points. All these developments contributed to a science which was increasingly accurate, increasingly automated and increasingly accessible. Before moving on to our underwater case studies, let us therefore have a brief look at this software package in particular. Light Absorption As light travels through water, it is gradually absorbed. Nevertheless, the data proved extremely challenging to work with, due to both the quality of the original picture sequence, and how these pictures were later digitised.