OUTDOOR TRAINING ENVIRONMENTS
We use real world and original data such as satellite images and height maps to reproduce real locations in 3D using artificial intelligence.
INDOOR TRAINING SCENARIOS
We generate diverse scenarios with varying perspectives while protecting consumers’ and companies’ data privacy.
CUSTOM TRAINING ENVIRONMENTS
We create custom synthetic training environments at any scale to address our client’s unique data science challenges.
Synthetic Data Generation using Customizable Environments
AI.Reverie offers a suite of simulated environments that empower the user to collect their own datasets based on the needs of their deep learning models. Several simulators are ready to deploy today to improve machine learning model accuracy.
Dynamic Objects & Scenarios for Training Data
AI.Reverie datasets can be populated with a large and diverse set of characters and objects that exactly represent those found in the real world.
- Vehicles, Inanimate Objects
- Basic Waypoint Movements
- Object Physics
- Character/Object Interaction
Configurable Sensors for Synthetic Data Generation
AI.Reverie simulators can include configurable sensors that allow machine learning scientists to capture data from any point of view. The sensors can also be set to reproduce a wide range of environmental conditions to further increase the diversity of your dataset.
- First Person, CCTV, Satellite Points of View
- Camera Sensors (RGB, PAN, LiDAR, Thermal)
- Intrinsic Parameters
- Extrinsic Parameters
- Sensor Noise
+ Video Transcript
We build photorealistic worlds that closely mimic real locations.
Botswana – Moremi Game Reserve:
- We provide fully annotated synthetic data in real time.
- 2D Bounding Boxes
- 3D Bounding Boxes
- Segmentation Masks
- Surface Normals
We Offer Diverse Images and Scenarios
- Simulated Outdoors
- Simulated Farms
- Simulated Homes
FREQUENTLY ASKED QUESTIONS ABOUT SYNTHETIC DATA GENERATION
What is Synthetic Data Generation?
Synthetic data is essentially data created in virtual worlds rather than collected from the real world. By simulating the real world, virtual worlds create synthetic data that is as good as, and sometimes better than, real data.
How Does Synthetic Data Work?
Synthetic data, as the name suggests, is data that is artificially created rather than being generated by actual events.
By simulating the real world, virtual worlds create synthetic data that is as good as, and sometimes better than, real data. As these worlds become more photorealistic, their usefulness for training dramatically increases.
It is often created with the help of algorithms and is used for a wide range of activities, including as test data for new products and tools, for model validation, and in AI model training.
What Are The Primary Benefits of Using Synthetic Data?
The success of deep learning has also bought an insatiable hunger for data.
AI.Reverie’s synthetic data platform generates photorealistic and diverse training data that significantly improves performance of computer vision algorithms.
- Unlimited Access
- Avoid privacy concerns associated with real images and videos
- Bootstrap algorithms when there is limited or no data
- Reduce data procurement timeline and costs
- Rich Diversity
- Produce data that includes all possible scenarios and objectS
- Perfect Annotation
- Procedurally generate labels
- Fast Training Cycles
- Improve model performance with AI.Reverie fine tuning and domain adaptation
What is The Value of a Synthetic Dataset?
- Gives Control: A customized synthetic dataset can be easily created based on unique objects, varying perspectives, rare scenarios and custom sensors.
- Lower Costs: Avoids the expensive process of annotating a real dataset by hand by generating annotations automatically in real time.
- Provides Scale: Delivers unlimited training data variables needed to get the best level of performance.
- Accelerates Development: Removes barriers to entry by providing easy access to training synthetic data needed to start training machine learning applications.
- Enhances Data Variables: Overcomes bias found in real data by adding diversity to help with generalization, regression, and correlation.