Improved Sensor Model for Realistic Synthetic Data Generation
Korbinian Hagn, Oliver Grau · 2021
Synthetic, i.e., computer generated-imagery (CGI) data is a key component for training and validating deep-learning-based perceptive functions due to its ability to simulate rare cases, avoidance of privacy issues and easy generation of huge datasets with pixel accurate ground-truth data. Recent simulation and rendering engines simulate already a wealth of realistic optical effects, but are mainly focused on the human perception system. But, perceptive functions require realistic images modeled with sensor artifacts as close as possible towards the sensor the training data has been recorded with.