Physically based synthetic image generation for machine learning: a review of pertinent literature
Dominik Schraml · 2019
The term deep learning is almost on everyone's lips these days, in the area of computer vision manly because of the great advances deep learning approaches have made amongst others in object detection and classification. For general object location or classification tasks there do exist several giant databases containing several millions of labeled images and several thousands of different labels like COCO and ImageNet. In contrast in industrial applications like quality inspection there do hardly ever exist such training data not only for reasons of confidentiality of trade secrets. An obvious way to remedy this deficiency is the synthetic creation of image data. Physically based rendering attempts to achieve photorealistic images by accurately simulating the ow of light of the real world according to various physical laws. Therefor multiple techniques like Ray Tracing and Path Tracing have been implemented and are becoming increasingly widespread as hardware performance increases. The intent of this article is to give a wide but nevertheless preferably comprehensive overview which approaches have been pursued in recent literature to generate realistic synthetic training images. The development of various rendering methods from rasterization to bidirectional Monte Carlo path tracing is outlined, as well as their differences and use. Along with the terminology a few mathematical foundations like the Bidirectional Reflectance Distribution Function (BRDF) are briefly described. Altogether specially concern is given to industrial data and quality control, comparing literature and the practical application of its results.