AI Approaches for Crime Scene Reconstruction: A Comprehensive Survey

Reconstructing a crime scene sits at the heart of forensic work. It lets experts’ piece together what unfolded, when it happened, and how things played out. Sure, traditional tools like LiDAR and photogrammetry get the job done with solid accuracy, but they come with a lot of manual work and, but they involve significant manual effort and lack immersive visualization. Now, artificial intelligence is shaking things up. With object detection models like YOLO, there’s a whole new level of automation and depth in visual analysis. This survey digs into how AI is transforming the process by utilizing visual evidence from photos, videos, and 3D scans, and using it to rebuild crime scenes in smarter ways. It covers the main strategies, points out what works well and what doesn’t, and introduces ForensiScan, a hybrid system that blends deep learning with immersive visualization. The paper doesn’t stop there, it also looks at how these systems get evaluated, the real-world hurdles they face, and where things are headed next as forensic tools keep getting smarter and more scalable.