Innovative Forensic Features and Blood Spatter 3D Reconstruction from Geospatial-driven Datasets
Received: 23 January 2025 | Revised: 28 March 2025 | Accepted: 7 April 2025 | Online: 4 June 2025
Corresponding author: Mohd Farid Mohd Ariff
Abstract
Crime scene documentation serves as the basis for reconstructing and interpreting evidence to provide forensic evaluation of crime events. The reliance on 2D photography for forensic features does not employ extensive documentation, as it limits the analytical properties of the captured data, such as 3D data fusion accurate scaling and innovative augmentation. This paper presents an innovative method to reconstruct forensic features and blood spatter using geospatial-driven datasets from multiple sources. The procedure involves a 3D reconstruction of a crime scene via geospatial techniques, namely Close-Range Photogrammetry (CRP) using an iPhone X smartphone camera and Terrestrial Laser Scanning (TLS) using a Faro Focus laser scanner. Meta Quest 2 was then used to transform the TLS point cloud into an immersive visualization. Meanwhile, an integrated point cloud model, transformed by the Iterative Closest Point (ICP) algorithm, was produced to examine the texture quality of the features being scanned, especially in blood spatter details. To determine the accuracy, the dimension measurements of the pieces of evidence were evaluated and analyzed using a two-sample t-test. The geometric property was extracted, showing that there were no significant differences between the mean-dimension measurements for all point samples. Additionally, the RGB values extracted from the point cloud datasets showed that CRP contributed to higher values compared to TLS. This shows that CRP facilitates more precise bloodstain pattern analysis compared to TLS.
Keywords:
crime scene reconstruction, blood spatter, point cloud fusion, iterative closest point, terrestrial laser scanning, virtual realityDownloads
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Copyright (c) 2025 Ahmad Firdaus Razali, Kyna Lani Edward, Mohd Farid Mohd Ariff, Nurul Shahirah Jasni, Norhadija Darwin, Zulkepli Majid
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