AI –Based 3D Crime Scene Reconstruction from Multi Model inputs

DOI

10.2991/978-94-6239-610-4_19How to use a DOI?

Keywords

Structure-from-Motion (SfM); Multi view stereo (MVS); mesh creation; cloud fusion; sematic segmentation

Abstract

This study presents a comprehensive analysis on the re-construction of crime scene by multiple inputs sources; using Colmap. Which mainly focuses on the basis of LiDAR data, notes, motion sensors, video footage, images from the crime scene. Investigation totally relies on examination, observation and analysis of the physical evidence. In many cases, the crucial evidence remains invisible to eyes, hard to find or vanished to the environmental conditions. Recent developments in AI, computer vision and sensory technology play vital role reconstruction of the crime scene. Methodology was data gathering, preprocessing, feature extraction, Structure-from-Motion (SfM), Multi view stereo (MVS), object identification, mesh creation, post process, and output. It also compares the AI– based construction and traditional construction methods based on authenticity, coherence, latent evidence identification. Phenomena aids in the flawless reconstruction of crime scene. We clearly write the evidence and examine the evidence with high authenticity, then we used cloud fusion and sematic segmentation. Additionally, we used tool like YOLOv3 (2018) [7]. This paper explores the crime scene reconstruction which aids forensic investigation and courtroom presentations, and overall crime scene analysis for effective investigation.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Myadagoni Rishik
AU  - Sanga Sree Vallabh
AU  - Jakka Srikanth Reddy
PY  - 2026
DA  - 2026/05/05
TI  - AI –Based 3D Crime Scene Reconstruction from Multi Model inputs
BT  - Proceedings of the First International Conference on Advances in Forensics and Cyber Technologies (ICFACT 2025)
PB  - Atlantis Press
SP  - 182
EP  - 194
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6239-610-4_19
DO  - 10.2991/978-94-6239-610-4_19
ID  - Rishik2026
ER  -