A validation study of the measurement accuracy of SCENE and SceneVision 3D software programs.

This descriptive study sought to determine the measurement accuracy of two 3D modeling software programs used in crime scene processing and reconstruction. These two programs are FARO's SCENE and 3rdTech's SceneVision 3D. This study compared the measurement difference means to guidelines published by the National Institute for Standards and Technology (NIST). A statistical analysis was performed by subtracting the manual measurement from the measurements from SCENE and SceneVision 3D. These differences were used in a paired t-test. The measurement difference means for each program were found to be within the NIST guidelines. The outcome of the paired t-test showed a statistical but not practical significance in the measurement differences. SCENE was found to be slightly more accurate than SceneVision 3D

Survey on Controlable Image Synthesis with Deep Learning

Image synthesis has attracted emerging research interests in academic and industry communities. Deep learning technologies especially the generative models greatly inspired controllable image synthesis approaches and applications, which aim to generate particular visual contents with latent prompts. In order to further investigate low-level controllable image synthesis problem which is crucial for fine image rendering and editing tasks, we present a survey of some recent works on 3D controllable image synthesis using deep learning. We first introduce the datasets and evaluation indicators for 3D controllable image synthesis. Then, we review the state-of-the-art research for geometrically controllable image synthesis in two aspects: 1) Viewpoint/pose-controllable image synthesis; 2) Structure/shape-controllable image synthesis. Furthermore, the photometrically controllable image synthesis approaches are also reviewed for 3D re-lighting researches. While the emphasis is on 3D controllable image synthesis algorithms, the related applications, products and resources are also briefly summarized for practitioners.Comment: 19 pages, 17 figure

Fire Pattern Analysis, Junk Science, Old Wives Tales, and Ipse Dixit: Emerging Forensic 3D Imaging Technologies to the Rescue?

Forensic science is undergoing a period of transformation as legal and scientific forces converge and force older forensic sciences toward a new scientific paradigm. Fire investigative undertakings are not an exception to this trend. Skeptical defense attorneys who routinely formulate astute Daubert challenges to contest the scientific validity and reliability of every major forensic science discipline are one catalyst to this revolution. Furthermore, a steady influx of novel scientific advances makes possible the formulation of consistent and scientifically-based quantitative forensic evidence analyses to overcome the “undervalidated and oversold” problems affecting many areas of forensic science

Point to Pipe: Automatic Reconstruction and Classification of Pipes Using Lasergrammetry and Thermogrammetry for Building Information Modeling (BIM)

Existing buildings account for 40% of global energy consumption, and two-thirds of them will be still be operational in 2050. As most of these buildings lack the needed documentation for energy upgrades, it is essential to understand and represent the current conditions of their envelopes and mechanical systems. This project proposed a skeleton-based application for reconstructing and classifying pipes in existing buildings using point clouds from laser scanners and thermal images for Building Information Modeling (BIM) applications. MATLAB and Dynamo were used to process and model this information in Revit. Initial results indicate that the application is robust to identifying pipes and connections, and that thermal images can be used to create sematic-rich models. These results can contribute to improving the capabilities of some of the commercially available software for pipe reconstruction in BIM and to expediting the digital reconstruction processes in existing buildings

3D Face Reconstruction: the Road to Forensics

3D face reconstruction algorithms from images and videos are applied to many fields, from plastic surgery to the entertainment sector, thanks to their advantageous features. However, when looking at forensic applications, 3D face reconstruction must observe strict requirements that still make its possible role in bringing evidence to a lawsuit unclear. An extensive investigation of the constraints, potential, and limits of its application in forensics is still missing. Shedding some light on this matter is the goal of the present survey, which starts by clarifying the relation between forensic applications and biometrics, with a focus on face recognition. Therefore, it provides an analysis of the achievements of 3D face reconstruction algorithms from surveillance videos and mugshot images and discusses the current obstacles that separate 3D face reconstruction from an active role in forensic applications. Finally, it examines the underlying data sets, with their advantages and limitations, while proposing alternatives that could substitute or complement them

3D Face Reconstruction: the Road to Forensics

3D face reconstruction algorithms from images and videos are applied to many fields, from plastic surgery to the entertainment sector, thanks to their advantageous features. However, when looking at forensic applications, 3D face reconstruction must observe strict requirements that still make its possible role in bringing evidence to a lawsuit unclear. An extensive investigation of the constraints, potential, and limits of its application in forensics is still missing. Shedding some light on this matter is the goal of the present survey, which starts by clarifying the relation between forensic applications and biometrics, with a focus on face recognition. Therefore, it provides an analysis of the achievements of 3D face reconstruction algorithms from surveillance videos and mugshot images and discusses the current obstacles that separate 3D face reconstruction from an active role in forensic applications. Finally, it examines the underlying data sets, with their advantages and limitations, while proposing alternatives that could substitute or complement them.Comment: The manuscript has been accepted for publication in ACM Computing Surveys. arXiv admin note: text overlap with arXiv:2303.1116

3D forensic crime scene reconstruction involving immersive technology: A systematic literature review

Recreation of 3D crime scenes is critical for law enforcement in the investigation of serious crimes for criminal justice responses. This work presents a premier systematic literature review (SLR) that offers a structured, methodical, and rigorous approach to understanding the trend of research in 3D crime scene reconstruction as well as tools, technologies, methods, and techniques employed thereof in the last 17 years. Major credible scholarly database sources, Scopus, and Google Scholar, which index journals and conferences that are promoted by entities such as IEEE, ACM, Elsevier, and SpringerLink were explored as data sources. Of the initial 17, 912 papers that resulted from the first search string, 258 were found to be relevant to our research questions after implementing the inclusion and exclusion criteria

Automated Semantic Content Extraction from Images

In this study, an automatic semantic segmentation and object recognition methodology is implemented which bridges the semantic gap between low level features of image content and high level conceptual meaning. Semantically understanding an image is essential in modeling autonomous robots, targeting customers in marketing or reverse engineering of building information modeling in the construction industry. To achieve an understanding of a room from a single image we proposed a new object recognition framework which has four major components: segmentation, scene detection, conceptual cueing and object recognition. The new segmentation methodology developed in this research extends Felzenswalb\u27s cost function to include new surface index and depth features as well as color, texture and normal features to overcome issues of occlusion and shadowing commonly found in images. Adding depth allows capturing new features for object recognition stage to achieve high accuracy compared to the current state of the art. The goal was to develop an approach to capture and label perceptually important regions which often reflect global representation and understanding of the image. We developed a system by using contextual and common sense information for improving object recognition and scene detection, and fused the information from scene and objects to reduce the level of uncertainty. This study in addition to improving segmentation, scene detection and object recognition, can be used in applications that require physical parsing of the image into objects, surfaces and their relations. The applications include robotics, social networking, intelligence and anti-terrorism efforts, criminal investigations and security, marketing, and building information modeling in the construction industry. In this dissertation a structural framework (ontology) is developed that generates text descriptions based on understanding of objects, structures and the attributes of an image