
Real-fidelity neural reconstructions and deterministic sensor simulation to build, test, and validate autonomous security and surveillance perception across every condition and threat scenario.

Edge Cases — Rare security events (intrusions, unauthorized access, suspicious behavior) are inherently dangerous and nearly impossible to stage realistically.
Observability — Model changes can behave differently across camera angles, lighting conditions, and facility layouts.
Safety — Testing threat detection with real actors in restricted areas carries significant safety and liability risk.
Solution — Continuously evaluate perception across threat scenarios, lighting, weather, and facility configurations in a virtual environment.

Staging realistic threat scenarios requires significant resources: actors, restricted access areas, coordinated timing across multiple cameras. Each iteration is costly and time-consuming to reproduce.

Security systems must perform reliably across different facilities, environments, lighting conditions, and camera configurations. Achieving consistent detection accuracy at scale through physical testing alone is impractical.
Ready to accelerate your security perception development? Tell us about your program and we’ll show you how PD can help you build safer, smarter detection systems.