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Aspects of Ergonomics in the Use of Respiratory Protective Devices
Published in Katarzyna Majchrzycka, Nanoaerosols, Air Filtering and Respiratory Protection, 2020
Precise examination of individual human anthropometric features is an attractive area of using computer-aided design (CAD) techniques and 3D scanning techniques in order to design structural elements of PPE, like respiratory protection. The development of computer techniques observed in recent years and the increasing technical capabilities of new optical and digitizing devices have a significant impact on the progress of structural simulation research and design of various products (D’Apuzzo 2005; Peng et al. 2012; Paula et al. 2014). The result is the ability to generate virtual models, e.g., ski jumping suits. Scanning accuracy of three-dimensional objects offered by currently available optical tools enables, e.g., the precise representation of shapes of faces (Joe et al. 2012; Rebar et al. 2010). The greatest advantages of the optical measurement method using a 3D scanner include, above all, high-quality mapping of the data recorded in a short time, high measurement accuracy, obtaining information about the geometry and texture of the entire surface of the measured object, and the possibility of direct comparison with the CAD data. Thanks to these features, 3D scanners can be used to scan shapes of different sizes and levels of complexity.
A 3D laser scanning technique and its application to rock mechanics and rock engineering
Published in Xia-Ting Feng, Rock Mechanics and Engineering, 2017
Laser scanning, sometimes referred to as lidar (light detection and ranging), is a relatively new technique for obtaining the digital data of an object: rather than making a single measurement like a laser rangefinder, it captures millions of measurements, called point clouds, by rotating mirrors (or the entire unit) to cover a large area of an object’s surface. A 3D scanner is a type of device that records the as-built situation of an object in terms of data about its shape, and possibly its appearance (i.e. intensity or color), by emitting light and detecting the reflection of that light in order to accurately determine the distance to the reflecting object. Nowadays, it is widely applied to different fields for 3D measurement, surveying, documentation and modeling for architecture and archaeology, 3D design, and 3D surveying. As an entire solution, 3D laser scanning techniques consist of both hardware and software, which involves not only the capture of scanning data with a scanning device, but also the processing of the captured data and generation of useful results.
A 3D laser scanning technique and its application to rock mechanics and rock engineering
Published in Xia-Ting Feng, Rock Mechanics and Engineering, 2017
Laser scanning, sometimes referred to as lidar (light detection and ranging), is a relatively new technique for obtaining the digital data of an object: rather than making a single measurement like a laser rangefinder, it captures millions of measurements, called point clouds, by rotating mirrors (or the entire unit) to cover a large area of an object’s surface. A 3D scanner is a type of device that records the as-built situation of an object in terms of data about its shape, and possibly its appearance (i.e. intensity or color), by emitting light and detecting the reflection of that light in order to accurately determine the distance to the reflecting object. Nowadays, it is widely applied to different fields for 3D measurement, surveying, documentation and modeling for architecture and archaeology, 3D design, and 3D surveying. As an entire solution, 3D laser scanning techniques consist of both hardware and software, which involves not only the capture of scanning data with a scanning device, but also the processing of the captured data and generation of useful results.
Development of a bridge maintenance system for prestressed concrete bridges using 3D digital twin model
Published in Structure and Infrastructure Engineering, 2019
Chang-Su Shim, Ngoc-Son Dang, Sokanya Lon, Chi-Ho Jeon
Three-dimensional scanning is a technique used to analyse and capture the shape of a real-world object using a 3D scanner. The result is computer-readable data, which can be saved, edited, and even 3D-printed. Various methods can be used for 3D scanning of objects and environments, but note that each method still consists of its own limitations and technological costs, which need to be considered. A detailed procedure of the generation of a reversed surface model is described in Section 3.2.1.
Solution of World Robot Challenge 2020 Partner Robot Challenge (Real Space)
Published in Advanced Robotics, 2022
Tomohiro Ono, Daiju Kanaoka, Tomoya Shiba, Shoshi Tokuno, Yuga Yano, Akinobu Mizutani, Ikuya Matsumoto, Hayato Amano, Hakaru Tamukoh
We propose an automatic dataset generation method using a physical simulator to solve this situation. Figure 3 shows an overview of the proposed method. In the proposed method, we use a real-time physics simulator, PyBullet [33]. The concrete procedure is as follows. Scanning 3D modelsA 3D scanner is used to create a 3D model of objects. We used the EinScan-SP [34].Initializing of the simulatorSet up the simulator environment for the assumed scene. In this example, we set up simple furniture such as a desk, chair, and shelves since we assume that the simulator will be used in a home environment. Changing the scene according to the assumed environment makes it possible to generate a high-quality dataset. Each piece of furniture is randomly rearranged after a certain number of data acquisitions.Spawning objectsThe 3D model created in Step 1 is generated in the simulator environment set up in Step 2. At this time, the position and orientation of the objects are randomly determined. In addition, the objects are generated in advance at a higher position than the ground and placed on the ground using physical operations to make the object placement closer to reality. After spawning objects, the physics operations are temporarily disabled to speed up the subsequent dataset generation process.Domain randomization, shooting, and annotationBy simulating the environment in a complex way, we improve the dataset's quality. Here, the background conditions (including furniture), objects, light source, and camera are randomly changed, and the image and annotation data are generated. These data is generated by using the rendering function of the simulator. The annotation data is output according to the Object Detection of COCO format [35, 36], used for bounding box detection, semantic segmentation and instance segmentation. In this case, we use monochromatic, gradient, checkered, and purlin noise images for the background and furniture textures. Apply to domain randomization, two hundred different images and annotation data are generated per one scene and then returned to Step 2.