Crime Scene Reconstruction with RGB-D Sensors
Abdenour Amamra, Yacine Amara, Khalid Boumaza, Aissa Benayad
Citation: Proceedings of the 2019 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 18, pages 391–396 (2019)
Abstract. Photographic surveying, a fundamental procedure in crime investigation, is typically performed using 2D cameras. Although useful, such cameras remain limited due to the lack of depth information. In this work, we propose a 3D reconstruction solution that leverages the advantages of cheap RGB-D sensors to create a 3D model of the crime scene and to provide the investigator with an interactive crime scenario simulation environment. A structure from motion approach is proposed in order to align the captured point clouds on each other using 3D key points. An iterative refinement and a global optimization algorithm are later adapted for the optimization of the registered 3D model, which is then triangulated before the underlying surface is reconstructed. The resulting model is used for interactive crime investigation and object dynamics simulation. The obtained results show the effectiveness of our solution with a visually appealing rendering, an accurate simulation and a quantitative error of less than 18cm for the $4m \times 4 m$ indoor scene. An accompanying video is provided in order to illustrate the processing pipeline (https://youtu.be/IYnJSNV7QkI).
- Simon Gibson and Toby Howard. “Interactive reconstruction of virtual environments from photographs, with application to scene-of-crime analysis”. In: Proceedings of the ACM symposium on Virtual reality software and technology. ACM. 2000, pp. 41–48.
- Giovanna Sansoni, Marco Trebeschi, and Franco Docchio. “State-of-the-art and applications of 3D imaging sensors in industry, cultural heritage, medicine, and criminal investigation”. In: Sensors 9.1 (2009), pp. 568–601.
- Erkan Bostanci. “3D reconstruction of crime scenes and design considerations for an interactive investigation tool”. In: arXiv preprint https://arxiv.org/abs/1512.03156 (2015).
- Stephen Se and Piotr Jasiobedzki. “Instant scene modeler for crime scene reconstruction”. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05)-Workshops. IEEE. 2005, pp. 123–123.
- Trung Kien Dang, Marcel Worring, and The Duy Bui. “A semi-interactive panorama based 3D reconstruction framework for indoor scenes”. In: Computer vision and image understanding 115.11 (2011), pp. 1516–1524.
- Ursula Buck et al. “Accident or homicide–virtual crime scene reconstruction using 3D methods”. In: Forensic science international 225.1-3 (2013), pp. 75–84.
- Andreas Georgopoulos and Elisavet Konstantina Stathopoulou. “Data acquisition for 3D geometric recording: state of the art and recent innovations”. In: Heritage and Archaeology in the Digital Age. Springer, 2017, pp. 1–26.
- Jason Ligon et al. “3D point cloud processing using spin images for object detection”. In: 2018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC). IEEE. 2018, pp. 731–736.
- A Amamra. “Robust 3D registration and tracking with RGBD sensors”. PhD thesis. Cranfield University, 2015.
- Yilin Liu et al. “Curvature feature extraction based ICP points cloud registration method”. In: Optoelectronic Imaging and Multimedia Technology V. Vol. 10817. International Society for Optics and Photonics. 2018, p. 1081707.