Faro Scene 2019.1 Download

I have the same problem, but with a twist. I have registered my scan in faro scene and it looks great. When i import the project and "skip registration" it doesn't get registered like it did in faro scene. most of the scans gets in to the right place but some twists around.

- Scanning: plan on three to ten scans of an area to capture point clouds. Scanner can produce xyz values and rgb values

- Optimizing data collection for your project: use the scanner's indoor and outdoor profiles to optimize collection at various distances (10m, 20m, further), and follow the "Scanner Positioning" guidelines here for scanning a single object: _Scanner_Best_Practices

- see also charts like this online for scan resolution for particular applications: -europe.com/en/news/scan-time-the-faro-focus-s-series

- FARO scanner shows time estimates for scans. To reduce scan time or total number of points collected, change parameters such as color(RGB) vs grayscale resolution, speed, night mode, HDR. Resolution guide (from _Scanner_Best_Practices - "Scanner Settings" - 1/1 or 1/2 - Objects and small areas, 1/4 or 1/5 - Outdoors and large, indoor spaces, 1/8 or 1/10 - Indoors and small, outdoor spaces. For example, use the "Indoor ...10m" profile for: Indoors, distance between scanner and object of interest is under 10m. Res: 1/8 Quality: 3X Color: On Sensors: On. See "On Site" section for more about weather(rain, wind, high heat) and surfaces(bright, reflective objects, glass, water, hot surfaces)

Faro Scene 2019.1 Download

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With FARO SCENE, users can convert scan data into usable CAD/BIM workflows with FARO As-BuiltTM Software, ensure construction quality control with BuildITTM Construction Software or reconstruct, analyze, and diagram forensic scenes with FARO Zone 3D Software.

The captured data can then be used to create a digital model, or toperform evaluation and analysis against an existing model for anythingrequiring highly detailed 3-D measurements, including factory planning,building-information modeling - as well as specialized applications rangingfrom land surveying, recreating accident sites and crime scenes, to digitallypreserving historical sites.

Bloodstains at crime scenes can be deposited or projected on many surface types, and the importance of Area of Origin (AO) calculations for impact stains are vital in the sequence of events. In the UK, wallpaper is a common material used to decorate walls. This study looked at the effect different wallpapers had on the calculated Area of Origin (AO) using FARO Zone 3D (FZ3D) software. A variety of wallpaper types were used, such as Foil, Printed, Vinyl, Washable, Woodchip and Anaglypta. These consisted of smooth and rough surface textures while the control was a plain painted wall. For each wallpaper type and control plain wall, six repeated impacts were conducted. An impact rig with a spring tension arm was fixed 45 cm from the X wall and 45 cm from the Y wall, and remained the same throughout the experiment, to resemble an impact blow for a bloodletting event. The location was also known to the analyst. AO error co-ordinates were measured directly in the FZ3D software to the known impact location, and the results were analysed. An overall 30 cm maximum allowable error from the known impact location was chosen since it was expected that textured surfaces would not perform as well. Nonetheless, 30 cm can still distinguish between a person that is low to the ground, kneeling or standing. The mean AO errors for each wallpaper type were Plain wall, 9.77 cm, Anaglypta wallpaper, 18.55 cm, Woodchip wallpaper, 13.99 cm, Washable wallpaper, 9.81 cm, Foil wallpaper, 10.82 cm, Printed wallpaper, 10.77 cm and Vinyl wallpaper, 9.59 cm. The maximum error for any one impact test was 24.81 cm which was within the chosen 30 cm limit. Wallpapers that had highly textured surfaces had the greatest errors. Also, FZ3D is shown to be an acceptable tool when analysing impact bloodstain patterns on different wallpaper types.

INTRODUCTION

In recent years, scientists and law enforcement agencies have been introduced to sophisticated technologies, which assist in bringing criminals to justice or to prevent an innocent individual from being convicted.1 Among the variety of recent additions of technology, experts and jurors see a dramatic change in metrological technologies - the scientific study of measurement. Innovations from forensic science research and development are introducing these new techniques for the purpose of crime solving, and increasing the reliability, efficiency and validation of forensic testing.2 Software packages such as HemoSpat,3 , BackTrack4 and FARO Scene5 have been independently tested, compared and validated through published, peer reviewed scientific articles. In addition, these software packages have been available for many years to allow for the analysis of bloodstains in a digital manner by utilising photographs and measurements to determine the Area of Origin (AO). The AO is defined as the threedimensional area from which impact stains originated. The AO is defined as the threedimensional area from which impact stains originated. The angle at which a blood droplet impacts the surface can be determined by a relationship, first observed by Dr. Victor Balthazard and subsequently formulated by Dr. Conrad Rizer. The angle () is determined by taking the arcsine of the ratio between the width (W) and length (L) of an individual blood droplet, ( = sin-1 (W/L). Crime scene investigators use this calculation when utilising the traditional stringing method to determine the angle of impact.

The Biological Evidence Preservation Handbook offers guidance for individuals involved in the collection, examination, tracking, packaging, storing, and disposition of biological evidence. This may include crime scene technicians, law enforcement officers, healthcare professionals, forensic scientists, forensic laboratory managers, evidence supervisors, property managers, storage facility personnel, lawyers, testifying experts, court staff members, and anyone else who may come in contact with biological evidence. While many of the recommendations relate to the physical storage, preservation, and tracking of evidence at the storage facility, this handbook also covers the transfer of the material between the storage facility and other locations and discusses how the evidence should be handled at these other locations.

Case study 2: big square hall, in which two walls are composed by glass windows. This scenario implies a difficulty to all scanning systems in general due to the reflectivity of glass, that provokes the appearance of a double registration for the points in the windows. The dimensions of the hall make it difficult for the mobile systems to extract features homogeneously distributed within the scene for a correct SLAM execution. In addition, the hall constitutes the entrance to the building, so the presence of people moving is unavoidable, with the consequent appearance of artifacts in the point cloud. Furniture such as tables, benches, standing posters and a vending machine have not been moved in order to test the performance of the system with elements that provoke occlusion.

Case study 3: corridor-room system, constructed with materials such as bricks and concrete. This case study presents no special requirements regarding material reflectivity but is chosen to study the capability of the system for scene reconstruction including turns and entrances in new spaces.

Point-cloud to point-cloud distances are computed based on a previous kd-tree division of the two point-clouds under analysis in order to determine the nearest neighbor point in one point cloud of each point in the reference point cloud (k = 1) for distance calculation [26]. Case study 2 shows the distance between the ground-truth and both mobile systems under study. Point clouds from the wearable mapping prototype and the Zeb-Revo have shown a mean distance with the point cloud of the FARO Focus, considered as ground-truth, of 0.11 0.23 m and 0.11 0.34 m, and mode distances (most populated distances) of 0.19 m and 0.03 m, respectively. The high value of the deviations (0.23 m and 0.34 m) is produced by the presence of more points in the scenes from the mobile devices, most of them corresponding to noise and increasing the error computed (Table 5 and Table 6). With these results, the Zeb-Revo shows higher closeness to the ground-truth and is thus chosen as reference for the analysis of the point cloud of the wearable prototype for the other case studies (Table 11 and Table 12).

The main dissimilarity between the point clouds of both mobile systems is the higher number of points in the Wearable Mapping Prototype; the hardware and software reasons have been identified and analyzed in the paper to reveal the main difference between the SLAM methodologies of the systems. While the presence of a high number of points can be inconvenient for visualization and imply higher needs for processing towards the modelling of the building, it also implies a higher detail for the modelling of furniture and other elements present in the scene. 006ab0faaa