Immersive Forensic Investigation

• Immersive forensic investigation is defined as the integration of 3D reconstructions, XR, AI, and advanced analytics to support interactive digital crime scene analysis.
• It employs techniques like photogrammetry, VR/AR visualization, and deep learning to automate evidence detection and enable collaborative scenario reconstruction.
• The approach enhances scene integrity, reduces physical contact risks, and speeds task completion by around 30%, ensuring rigorous forensic documentation.

Immersive forensic investigation is the integration of high-fidelity three-dimensional reconstructions, extended reality (XR) technologies, AI, and advanced data analytics, designed to support the collection, analysis, and presentation of forensic evidence in digitally mediated, spatially interactive environments. These systems enable forensic practitioners to virtually examine crime scenes, conduct medico-legal autopsies, perform collaborative scenario reconstruction, and automate parts of documentation—leveraging both virtual reality (VR) and augmented reality (AR), as well as AI-powered decision support and automated object detection (Pooryousef et al., 17 Dec 2025, Smyth et al., 2018, Zappalà et al., 27 Sep 2024, Smyth et al., 2018, Bostanci, 2015).

1. Scope and Conceptual Framework

Immersive forensic investigation encompasses the application of XR environments to two principal domains: (a) the augmentation of autopsy workflows in biohazard-controlled settings, and (b) the digital reconstruction and real-time analysis of crime scenes. The technological ecosystem consists of head-worn XR displays, virtualized scene models, generative-AI–assisted reporting, and collaborative multi-user interfaces. The practice is demarcated by its commitment to metric spatial accuracy, scene integrity (zero-contact documentation), and strict adherence to forensic chain-of-custody principles.

Specifically, immersive tools are utilized to:

• Render high-accuracy photogrammetric or synthetic reconstructions in VR/AR for detailed scene interrogation.
• Enable spatial-temporal replay and animation of incidents for hypothesis testing.
• Integrate deep learning–based object detectors to automate evidence discovery and categorization.
• Serve as a safe platform for decision support and training, especially in rare or hazardous CBRNE (chemical, biological, radiological/nuclear, explosive) scenarios (Pooryousef et al., 17 Dec 2025, Smyth et al., 2018, Zappalà et al., 27 Sep 2024).

2. Technical Components and System Architectures

The technical realization of immersive forensic investigation depends on tightly-coupled digital pipelines, detailed as follows:

2.1 Photogrammetric 3D Reconstruction

• Acquisition: High-resolution imagery (≥1280×720) using calibrated devices.
• Keyframe Extraction: Inlier-threshold methods (t = 200) to reduce redundant frames.
• Structure-from-Motion (SfM) and Bundle Adjustment: Calibration via minimization of reprojection error

$$\{K_i, R_i, t_i, X_j\}^* = \arg\min_{K_i,R_i,t_i,X_j} \sum_{i=1}^{N_c}\sum_{j=1}^{N_p} \|x_{ij} - \pi(K_i[R_i|t_i]X_j)\|^2$$

and subsequent dense multi-view stereo for point cloud reconstruction (Bostanci, 2015, Zappalà et al., 27 Sep 2024).

• Alignment: ICP-based fusion of clusters (RMSE < 1 mm), yielding watertight textured meshes suitable for immersive visualization.

2.2 Immersive Visualization and XR Integration

• Engine: Unreal Engine or Unity, supporting physics-based rendering, photorealistic lighting, and VR/AR plugins.
• User Interaction: Navigation, region selection, annotation, and measurement are implemented via controller-based raycasting and touchless modalities (gaze, hand-tracking); real-time overlays display evidence labels, risk scores, and analytic results (Pooryousef et al., 17 Dec 2025, Smyth et al., 2018, Bostanci, 2015).
• System Architecture: Modular, RESTful/WebSocket API-interconnected components for RAV/robotic control, analytics, and decision support. Multi-user networking enables collaborative immersive analysis.

2.3 Deep Learning and Automated Evidence Analytics

• Object Detection: Mask R-CNN and Faster R-CNN architectures (e.g., ResNet-50 backbone) used for semantic instance segmentation, with loss

$$L({p_i},{t_i}) = \frac{1}{N_{cls}} \sum_i L_{cls}(p_i,p_i^*) + \lambda \frac{1}{N_{reg}} \sum_i p_i^* L_{reg}(t_i, t_i^*)$$

(Zappalà et al., 27 Sep 2024, Smyth et al., 2018, Smyth et al., 2018).

• Anomaly Detection: Statistical (Mahalanobis distance) and deep autoencoder-based methods for identifying outlier regions or novel scene configurations (Smyth et al., 2018).
• Performance: [email protected] for Mask R-CNN reaches 0.78 (real-world set), with VR-accelerated workflows yielding a 30% reduction in task completion time (Smyth et al., 2018, Zappalà et al., 27 Sep 2024).

2.4 Decision-Support Tools and Probabilistic Reasoning

• Bayesian Inference: Dynamic Bayesian models update threat probabilities with each sensor reading or analytic cue. Posterior at time $t$:

$$\text{Posterior}_t(\theta) = \alpha P(e_t | \theta)\,\text{Posterior}_{t-1}(\theta)$$

implemented in BLOG for open-world uncertainty (Smyth et al., 2018, Smyth et al., 2018).

• Document Retrieval: TF–IDF and BM25 ranking schemes over SOPs and forensic guidance, indexed in Elasticsearch and updated in real-time as threat models evolve.

3. Workflow Integration and Human Factors

Implementing immersive forensic workflows requires stringent attention to the context-specific requirements of domain experts:

• High-fidelity prototypes are mandatory to convey nuanced spatial interaction paradigms, overcoming the abstraction gap seen in low-fidelity sketches (Pooryousef et al., 17 Dec 2025).
• Biohazard mitigation is addressed with touchless XR interactions, critical in autopsy or contaminated environments (Pooryousef et al., 17 Dec 2025).
• Generative-AI–assisted documentation reduces reporting time by ~25%, but introduces accountability questions for automatically generated text (Pooryousef et al., 17 Dec 2025).
• User studies highlight expert preference for direct-action modalities (look or gesture to summon spatial overlays) and utility of temporal scene animations for hypothesis revision (Pooryousef et al., 17 Dec 2025).
• Workflow integration includes report dashboards, multi-tiered stakeholder perspectives, and the maintenance of domain-specific lexicons within interfaces.

4. Evaluation Metrics and Experimental Outcomes

Systematic evaluation uses both quantitative and qualitative criteria:

Metric/Result	System/Method	Reference
[email protected] (object detection)	Mask R-CNN	(Smyth et al., 2018, Smyth et al., 2018)
Task completion speedup (VR vs. 2D)	30% faster	(Smyth et al., 2018)
Immersion (ITC-Sense score)	5.8 (VR), 4.2 (2D map)	(Smyth et al., 2018)
Annotation/measurement accuracy	≤2 mm	(Bostanci, 2015)
Report drafting time reduction	25%	(Pooryousef et al., 17 Dec 2025)
Risk inference calibration (Brier)	0.12 (adaptive) vs. 0.24	(Smyth et al., 2018)
Contamination risk	Zero physical contact	(Zappalà et al., 27 Sep 2024)

Qualitative findings also reveal improved hypothesis generation, stakeholder collaboration, and reduced subjective bias in documentation processes.

5. Ethical, Legal, and Practical Barriers

Deployment of immersive forensic systems involves substantive challenges:

• Ethics approval for studies is notably delayed in non-academic institutions (four months vs. two weeks in academia) (Pooryousef et al., 17 Dec 2025).
• Data confidentiality and privacy are paramount: forensic images and telemetry can contain sensitive case information, necessitating selective data logging, strict control of microphone and gaze data, and robust management of participant privacy (Pooryousef et al., 17 Dec 2025).
• Small, homogeneous expert samples necessitate that findings be labeled “exploratory,” with explicit triangulation via interviews and supplemental video-based evaluation.

Procedural recommendations include offering contributorship credit judiciously, planning for extended approval timelines, and bridging the XR experience gap with Wizard-of-Oz prototyping (Pooryousef et al., 17 Dec 2025).

6. Extensions, Limitations, and Future Directions

Current implementations are limited by:

• Restricted generalization of object detectors (COCO-class pretraining, limited domain-specific labels) (Zappalà et al., 27 Sep 2024).
• System latencies (~10 s per image for object detection), inadequate for seamless streaming VR (Zappalà et al., 27 Sep 2024).
• Potential for false negatives in niche forensic evidence classes due to lack of specialized datasets (Zappalà et al., 27 Sep 2024, Bostanci, 2015).

Anticipated advances include:

• Development and fine-tuning of dedicated crime-scene evidence datasets (blood spatters, cartridge cases).
• GPU-optimized inference paths (TensorRT, ONNX) for real-time detection.
• Multi-user collaborative VR sessions with synchronized annotation and analytic overlays.
• Automated scene-to-scene temporal change detection, supporting audit trails and integrity checks.

A plausible implication is that as immersive systems mature, forensic investigations will transition further toward virtualized, low-risk, and rigorously auditable processes, with AI-augmented workflows reducing subjective bias and accelerating evidence chain management (Zappalà et al., 27 Sep 2024, Smyth et al., 2018, Pooryousef et al., 17 Dec 2025).