Endless Applications.
The right synthetic data makes all the difference in advanced perception and machine learning. It can fill in data gaps or complement real-world footage in a variety of industries. At ANYVERSE we can simulate any scenario and cover a whole range of corner cases with accuracy that consequently boosts your AI.
AV/ADAS
ANYVERSE supports a wide range of applications for autonomous vehicle and drive-assist development. Firstly, we can model any scenario using a geographically-stylized urban, suburban, rural and highway environments. Secondly, we can randomly add high-quality vehicles and pedestrians to follow specific behaviors.
+ Classification and detection of vehicles and pedestrians
+ Detection of empty spaces in parking lots
+ Classification and detection of traffic lights
+ Corner cases (i.e. pedestrians at night)
UAV
Unmanned Aerial Vehicles (UAV) are widely used across different industries. ANYVERSE supports drones as a kind of an ego-vehicle, with an arbitrary number of cameras in defined 3D fly-thru scenarios. What is more, we can add to the scenes custom ground-truth data for objects of interest or defective parts.
+ Package delivery in urban/suburban areas
+ Infrastructure inspection
+ Urban/suburban surveillance
+ Accidents in streets or highways
+ Airport-related security operations
Smart Cameras
Synthetic data may prove useful for training smart cameras inside vehicles or other indoor scenarios. ANYVERSE can, for example, add a rich database of 3D people to these scenarios. Variability then applies to lighting, objects, textures, poses and behaviors.
+ Warning distracted or drowsy drivers
+ Detecting serious issues such as a child removing their seat belt
+ Detecting certain behaviors or dangerous situations in home environments
Perception Developers
ANYVERSE provides perception developers with simulated data produced with different sensor models in different positions. This helps them design and optimize new perception systems. Moreover, physics-based camera and LiDAR models mirror the real devices and in turn produce synthetic data exactly as the system would do in the real world.
+ Test different camera models and configurations
+ Try a combination of LiDAR and camera data
+ Examine different combinations of ground- truth data
Any synthetic data questions, drop us a message!