Duminil Alexandra, Ieng Sio-Song, Gruyer Dominique
Department of Components and Systems (COSYS)/Perceptions, Interactions, Behaviour and Simulations of Road and Street Users Laboratory (PICS-L)/Gustave Eiffel University, F-77454 Marne-la-Vallée, France.
Sensors (Basel). 2024 Apr 11;24(8):2463. doi: 10.3390/s24082463.
Generating realistic road scenes is crucial for advanced driving systems, particularly for training deep learning methods and validation. Numerous efforts aim to create larger and more realistic synthetic datasets using graphics engines or synthetic-to-real domain adaptation algorithms. In the realm of computer-generated images (CGIs), assessing fidelity is challenging and involves both objective and subjective aspects. Our study adopts a comprehensive conceptual framework to quantify the fidelity of RGB images, unlike existing methods that are predominantly application-specific. This is probably due to the data complexity and huge range of possible situations and conditions encountered. In this paper, a set of distinct metrics assessing the level of fidelity of virtual RGB images is proposed. For quantifying image fidelity, we analyze both local and global perspectives of texture and the high-frequency information in images. Our focus is on the statistical characteristics of realistic and synthetic road datasets, using over 28,000 images from at least 10 datasets. Through a thorough examination, we aim to reveal insights into texture patterns and high-frequency components contributing to the objective perception of data realism in road scenes. This study, exploring image fidelity in both virtual and real conditions, takes the perspective of an embedded camera rather than the human eye. The results of this work, including a pioneering set of objective scores applied to real, virtual, and improved virtual data, offer crucial insights and are an asset for the scientific community in quantifying fidelity levels.
生成逼真的道路场景对于先进的驾驶系统至关重要，特别是对于深度学习方法的训练和验证。许多努力旨在使用图形引擎或合成到真实域适应算法来创建更大、更逼真的合成数据集。在计算机生成图像（CGI）领域，评估逼真度具有挑战性，涉及客观和主观两个方面。我们的研究采用了一个全面的概念框架来量化RGB图像的逼真度，这与现有的主要针对特定应用的方法不同。这可能是由于数据的复杂性以及所遇到的可能情况和条件范围巨大。本文提出了一组不同的指标来评估虚拟RGB图像的逼真度水平。为了量化图像逼真度，我们从局部和全局角度分析了图像中的纹理和高频信息。我们的重点是现实和合成道路数据集的统计特征，使用了来自至少10个数据集的超过28000张图像。通过深入研究，我们旨在揭示有助于道路场景中数据现实感客观感知的纹理模式和高频成分的见解。这项研究从嵌入式摄像头而非人眼的角度探索虚拟和真实条件下的图像逼真度。这项工作的结果，包括应用于真实、虚拟和改进虚拟数据的一套开创性的客观分数，提供了关键见解，是科学界量化逼真度水平的一项宝贵资产。
