MONOCULAR DEPTH ESTIMATION FOR NIGHT-TIME IMAGES

Abstract. Depth estimation plays a pivotal role in numerous computer vision applications. However, depth estimation networks trained exclusively on daytime images tend to yield poor performance when applied to nighttime scenarios due to domain differences and variations in scene characteristics. In order to address this limitation, we conducted experiments involving the creation of a synthetic nighttime dataset by employing image translation techniques through a generative network. Subsequently, we utilized the generated images to fine-tune the depth estimation network, aiming to investigate the potential for enhancing task performance using generated data. We evaluated our approach by testing with the generated data, and we observed a noticeable improvement in the depth estimation task both before and after fine-tuning. Consequently, our approach yields results that are comparable to those achieved by networks specifically designed for daytime prediction. These findings highlight the effectiveness of utilizing synthetic data to enhance the performance of depth estimation tasks, particularly in nighttime settings.