An automated scenario engine that generates thousands of realistic event variations by randomizing lighting, occlusion, layout, and sensor noise for robust AI training.
OVERVIEW
Scenario Generator takes the spatial model from Space Builder and creates thousands of event scenario variations. By applying domain randomization -- changing lighting conditions, object placement, occlusion patterns, and sensor noise -- it ensures AI models encounter the full range of real-world conditions during training. Each scenario is labeled using perception-only criteria: only what a camera can actually observe gets labeled, producing training data that matches real deployment conditions.
TECHNOLOGY
Automated variation of lighting, occlusion, layout, and noise parameters to generate diverse training scenarios from a single spatial model.
Labels events based only on what is observable from the camera viewpoint, matching real-world sensor limitations.
Domain-specific language for defining event types, triggers, and state transitions within the spatial model.
High-performance simulation engine that renders scenarios at scale with deterministic reproducibility.
CAPABILITIES
Automatically vary ambient lighting, shadows, and time-of-day conditions across generated scenarios.
Simulate partial and full object occlusion to train models that handle real-world visual obstruction.
Randomize furniture and object placement within zones to handle environment changes after initial setup.
Add realistic camera noise, motion blur, and compression artifacts to training data.
Generate hundreds to thousands of scenario variations in parallel for rapid dataset creation.
Define new event types with custom triggers, durations, and state-change criteria using the event DSL.
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