Multiview Generative AI (GenAI)

Important technical features

Realistic data simulation

Generation of synthetic data with identical dimensionality to real data

Fine-tuned data variability

Targeted control of shape, size, noise and lighting conditions to generate highly variable data sets.

Model optimization through targeted error generation

Identification and correction of model weaknesses by generating specific, erroneous images.

Scalable data generation

On-demand generation of thousands or more data sets.

Refined data selection

Post-generation refinement approaches to select the optimal training images.

Integration options

Synthetic data for multi-view AI

Synthetic data, generated by GenAI and refined by data curation, can be used to train multi-view AI models.

Acceleration through light field data

The K|Lens light field sensors (1inch or HighRes) provide multi-view data, allowing GenAI to learn faster and generate more relevant synthetic data.

Compatibility with scanner sensor data

Data sets from the K|Lens scanner sensor can also be easily processed.

Advantages

Reduced data collection costs

Instead of collecting data for weeks, thousands of synthetic images can be generated within a day and model performance can be improved.

No forced defect production

No need to intentionally produce defective products to increase the training dataset.

Efficient processing of rare defects

For rare defect types, you do not need to provide large sample sizes as GenAI effectively increases data representation.

Accelerated project cycles

Shorter project lifecycles through accelerated data generation and model training.

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