Synthetic data photorealism for computer vision

In this section, you will learn why photorealism is essential in computer vision. Photorealism of synthetic data is one of the main factors that mitigates the domain gap between real and synthetic data. Thus, training computer vision models on photorealistic synthetic data improves the performance of these models on real data. For more details, please refer to Hypersim: A Photorealistic Synthetic Dataset for Holistic Indoor Scene Understanding (https://arxiv.org/abs/2011.02523) and A Review of Synthetic Image Data and Its Use in Computer Vision (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9698631). Additionally, synthetic data can be used to evaluate computer vision algorithms. However, evaluating these models on non-photorealistic synthetic data may cause these models to show poor performance not because of the challenging nature of the test scenarios but because of the domain gap itself. Thus, photorealistic synthetic data is essential...