Feasibility of Neural Radiance Fields for Crime Scene Video Reconstruction

TL;DR

This paper evaluates the potential of Neural Radiance Fields (NeRF) models for reconstructing crime scenes from videos, focusing on innovations like multi-object synthesis, deformable synthesis, and lighting.

Contribution

It analyzes the feasibility of applying NeRF variations to crime scene reconstruction, highlighting key innovations and assessing their suitability.

Findings

NeRF models show promise for detailed scene reconstruction.

Multi-object and deformable synthesis are critical for crime scene accuracy.

Lighting considerations significantly impact reconstruction quality.

Abstract

This paper aims to review and determine the feasibility of using variations of NeRF models in order to reconstruct crime scenes given input videos of the scene. We focus on three main innovations of NeRF when it comes to reconstructing crime scenes: Multi-object Synthesis, Deformable Synthesis, and Lighting. From there, we analyse its innovation progress against the requirements to be met in order to be able to reconstruct crime scenes with given videos of such scenes.