Feasibility of Neural Radiance Fields for Crime Scene Video Reconstruction
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 11 of its 12 references that are in the map. First posted on arXiv 11 Jul 2024.
Builds on
11 of 12 references.
References · 11
- 2020NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collectionscited by 1,230
- 2023Deformable 3D Gaussians for High-Fidelity Monocular Dynamic Scene Reconstructioncited by 753
- 2021Depth-supervised NeRF: Fewer Views and Faster Training for Freecited by 732
- 2018Real-world Anomaly Detection in Surveillance Videoscited by 570
- 2020NeRV: Neural Reflectance and Visibility Fields for Relighting and View Synthesiscited by 493
- 2020NeRD: Neural Reflectance Decomposition from Image Collectionscited by 446
- 2021Advances in Neural Renderingcited by 365
- 2021NeX: Real-time View Synthesis with Neural Basis Expansioncited by 215
- 20243D Geometry-aware Deformable Gaussian Splatting for Dynamic View Synthesiscited by 109
- 2023Neural Parametric Gaussians for Monocular Non-Rigid Object Reconstructioncited by 60
- 2023Total-Recon: Deformable Scene Reconstruction for Embodied View Synthesiscited by 20
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