Deep Learning-Based Point Upsampling for Edge Enhancement of 3D-Scanned Data and Its Application to Transparent Visualization

Large-scale 3D-scanned point clouds enable the accurate and easy recording of complex 3D objects in the real world. The acquired point clouds often describe both the surficial and internal 3D structure of the scanned objects. The recently proposed edge-highlighted transparent visualization method is effective for recognizing the whole 3D structure of such point clouds. This visualization utilizes the degree of opacity for highlighting edges of the 3D-scanned objects, and it realizes clear transparent viewing of the entire 3D structures. However, for 3D-scanned point clouds, the quality of any edge-highlighting visualization depends on the distribution of the extracted edge points. Insufficient density, sparseness, or partial defects in the edge points can lead to unclear edge visualization. Therefore, in this paper, we propose a deep learning-based upsampling method focusing on the edge regions of 3D-scanned point clouds to generate more edge points during the 3D-edge upsampling task. The proposed upsampling network dramatically improves the point-distributional density, uniformity, and connectivity in the edge regions. The results on synthetic and scanned edge data show that our method can improve the percentage of edge points more than 15% compared to the existing point cloud upsampling network. Our upsampling network works well for both sharp and soft edges. A combined use with a noise-eliminating filter also works well. We demonstrate the effectiveness of our upsampling network by applying it to various real 3D-scanned point clouds. We also prove that the improved edge point distribution can improve the visibility of the edge-highlighted transparent visualization of complex 3D-scanned objects

Using the iPhone's LiDAR technology to capture 3D forensic data at crime and crash scenes

Background: Three-dimensional (3D) documentation of crime and crash scenes is common practice during forensic and medicolegal investigations. Such documentation at a scene is usually carried out by specially trained personnel using various 3D imaging devices and methods, such as terrestrial laser scanners. Unfortunately, this causes the implementation of 3D documentation at the scenes to be expensive and not readily accessible. In 2020, Apple introduced a light detection and ranging (LiDAR) sensor into their high-end mobile devices. In 2022, Recon-3D, an iOS application (app), was launched. This app turns an iPhone or iPad into a 3D scanner and is specifically targeted at crime and crash scene applications. Objectives: The aim of this study was to test the Recon-3D app based on exemplary scenarios to see whether this technology is generally applicable to document crime or crash scenes. Materials and Methods: An iPhone 13 Pro in combination with the Recon-3D app was used to document two indoor scenarios, a mock-up crime scene and a garage, as well as an outdoor scenario of a parked car. Each scenario was documented multiple times. Results: On average, data acquisition for one scene took less than 2 min. Known distances within the scenes were measured with a mean absolute error of 0.22 cm and a standard deviation of 0.18 cm. Conclusion: The imaging workflow was simple and quick, enabling any person to perform 3D documentation at a crime or crash scene. Overall, Recon-3D appeared to be a useful application for forensic investigators

Enhancing Traffic Safety in Unpredicted Environments with Integration of ADAS Features with Sensor Fusion in Intelligent Electric Vehicle Platform with Implementation of Environmental Mapping Technology

A major objective on society is to reduce the number of accidents and fatalities on the road for drivers, and pedestrians. Therefore, the automotive engineering field is working on this problem through the development and integration of safety technologies such as advanced driving assistance systems. For this reason, this work was intended to develop and evaluate the performance of different ADAS features and IV technologies under unexpected scenarios. This by the development of safety algorithms applied to the intelligent electric vehicle designed and built in this work, through the use of ADAS sensors based on sensor fusion. Evaluation of AEB, PA, steering by wire, and machine learning based distance predictions, has been studied in this work bringing a contribution to driver safety and the well-being of pedestrians. Based on this work, the enhancement of distance precision of ADAS features with a percentage error of 3.89% compared to average of raw sensors data was found as well as an study of impact of color in LiDAR data quality

Automatic segmentation and reconstruction of traffic accident scenarios from mobile laser scanning data

Virtual reconstruction of historic sites, planning of restorations and attachments of new building parts, as well as forest inventory are few examples of fields that benefit from the application of 3D surveying data. Originally using 2D photo based documentation and manual distance measurements, the 3D information obtained from multi camera and laser scanning systems realizes a noticeable improvement regarding the surveying times and the amount of generated 3D information. The 3D data allows a detailed post processing and better visualization of all relevant spatial information. Yet, for the extraction of the required information from the raw scan data and for the generation of useable visual output, time-consuming, complex user-based data processing is still required, using the commercially available 3D software tools. In this context, the automatic object recognition from 3D point cloud and depth data has been discussed in many different works. The developed tools and methods however, usually only focus on a certain kind of object or the detection of learned invariant surface shapes. Although the resulting methods are applicable for certain practices of data segmentation, they are not necessarily suitable for arbitrary tasks due to the varying requirements of the different fields of research. This thesis presents a more widespread solution for automatic scene reconstruction from 3D point clouds, targeting street scenarios, specifically for the task of traffic accident scene analysis and documentation. The data, obtained by sampling the scene using a mobile scanning system is evaluated, segmented, and finally used to generate detailed 3D information of the scanned environment. To realize this aim, this work adapts and validates various existing approaches on laser scan segmentation regarding the application on accident relevant scene information, including road surfaces and markings, vehicles, walls, trees and other salient objects. The approaches are therefore evaluated regarding their suitability and limitations for the given tasks, as well as for possibilities concerning the combined application together with other procedures. The obtained knowledge is used for the development of new algorithms and procedures to allow a satisfying segmentation and reconstruction of the scene, corresponding to the available sampling densities and precisions. Besides the segmentation of the point cloud data, this thesis presents different visualization and reconstruction methods to achieve a wider range of possible applications of the developed system for data export and utilization in different third party software tools

Modeling and Simulation in Engineering

This book provides an open platform to establish and share knowledge developed by scholars, scientists, and engineers from all over the world, about various applications of the modeling and simulation in the design process of products, in various engineering fields. The book consists of 12 chapters arranged in two sections (3D Modeling and Virtual Prototyping), reflecting the multidimensionality of applications related to modeling and simulation. Some of the most recent modeling and simulation techniques, as well as some of the most accurate and sophisticated software in treating complex systems, are applied. All the original contributions in this book are jointed by the basic principle of a successful modeling and simulation process: as complex as necessary, and as simple as possible. The idea is to manipulate the simplifying assumptions in a way that reduces the complexity of the model (in order to make a real-time simulation), but without altering the precision of the results

Microscope Embedded Neurosurgical Training and Intraoperative System

In the recent years, neurosurgery has been strongly influenced by new technologies. Computer Aided Surgery (CAS) offers several benefits for patients\u27 safety but fine techniques targeted to obtain minimally invasive and traumatic treatments are required, since intra-operative false movements can be devastating, resulting in patients deaths. The precision of the surgical gesture is related both to accuracy of the available technological instruments and surgeon\u27s experience. In this frame, medical training is particularly important. From a technological point of view, the use of Virtual Reality (VR) for surgeon training and Augmented Reality (AR) for intra-operative treatments offer the best results. In addition, traditional techniques for training in surgery include the use of animals, phantoms and cadavers. The main limitation of these approaches is that live tissue has different properties from dead tissue and that animal anatomy is significantly different from the human. From the medical point of view, Low-Grade Gliomas (LGGs) are intrinsic brain tumours that typically occur in younger adults. The objective of related treatment is to remove as much of the tumour as possible while minimizing damage to the healthy brain. Pathological tissue may closely resemble normal brain parenchyma when looked at through the neurosurgical microscope. The tactile appreciation of the different consistency of the tumour compared to normal brain requires considerable experience on the part of the neurosurgeon and it is a vital point. The first part of this PhD thesis presents a system for realistic simulation (visual and haptic) of the spatula palpation of the LGG. This is the first prototype of a training system using VR, haptics and a real microscope for neurosurgery. This architecture can be also adapted for intra-operative purposes. In this instance, a surgeon needs the basic setup for the Image Guided Therapy (IGT) interventions: microscope, monitors and navigated surgical instruments. The same virtual environment can be AR rendered onto the microscope optics. The objective is to enhance the surgeon\u27s ability for a better intra-operative orientation by giving him a three-dimensional view and other information necessary for a safe navigation inside the patient. The last considerations have served as motivation for the second part of this work which has been devoted to improving a prototype of an AR stereoscopic microscope for neurosurgical interventions, developed in our institute in a previous work. A completely new software has been developed in order to reuse the microscope hardware, enhancing both rendering performances and usability. Since both AR and VR share the same platform, the system can be referred to as Mixed Reality System for neurosurgery. All the components are open source or at least based on a GPL license

Training of Crisis Mappers and Map Production from Multi-sensor Data: Vernazza Case Study (Cinque Terre National Park, Italy)

This aim of paper is to presents the development of a multidisciplinary project carried out by the cooperation between Politecnico di Torino and ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action). The goal of the project was the training in geospatial data acquiring and processing for students attending Architecture and Engineering Courses, in order to start up a team of "volunteer mappers". Indeed, the project is aimed to document the environmental and built heritage subject to disaster; the purpose is to improve the capabilities of the actors involved in the activities connected in geospatial data collection, integration and sharing. The proposed area for testing the training activities is the Cinque Terre National Park, registered in the World Heritage List since 1997. The area was affected by flood on the 25th of October 2011. According to other international experiences, the group is expected to be active after emergencies in order to upgrade maps, using data acquired by typical geomatic methods and techniques such as terrestrial and aerial Lidar, close-range and aerial photogrammetry, topographic and GNSS instruments etc.; or by non conventional systems and instruments such us UAV, mobile mapping etc. The ultimate goal is to implement a WebGIS platform to share all the data collected with local authorities and the Civil Protectio

Unmanned Robotic Systems and Applications

This book presents recent studies of unmanned robotic systems and their applications. With its five chapters, the book brings together important contributions from renowned international researchers. Unmanned autonomous robots are ideal candidates for applications such as rescue missions, especially in areas that are difficult to access. Swarm robotics (multiple robots working together) is another exciting application of the unmanned robotics systems, for example, coordinated search by an interconnected group of moving robots for the purpose of finding a source of hazardous emissions. These robots can behave like individuals working in a group without a centralized control