Study on the Influence of Texture Variation on the Validation Performance of a Synthetically Trained Object Detector

Abstract

In recent years, the utilization of synthetic data for the training of Deep Learning (DL) approaches has emerged as a valid alternative to the costly process of real data acquisition. Yet, the influence of the sim-to-real gap on the model performance still poses an obstacle to the broader usage of synthetic data. To investigate the major contributing factors, this study focuses on the influence of texture variation as a first step. Examining different strategies for generating synthetic validation sets for the training process of an object detector, the results of this study indicate that the sole influence of textures is insufficient to cause the observable performance gap alone.