用太阳高度角生成真实感日间光照，无需人工标注。
Solar Altitude Guided Scene Illumination
- 以太阳高度角为全局条件，自动生成不同光照下的图像
- 在扩散模型中精准还原光照特征与依赖光照的噪声模式
- 适合自动驾驶数据增强、光照可控的合成数据生成
安全可靠的自动驾驶功能发展严重依赖大规模高质量传感器数据。然而，真实世界数据采集需大量人力，且受标注成本、驾驶员安全规范和场景覆盖范围限制。因此，众多研究致力于条件化生成合成摄像头数据。我们发现现有研究在日间变化建模上存在显著空白，可能源于可用标签稀缺。为此，我们提出将太阳高度角作为全局条件变量，该值可由经纬度坐标与当地时间直接计算，无需人工标注。本工作还引入一种定制化归一化方法，以应对光照对微小高度角变化的敏感性。我们在扩散模型框架下验证了其准确捕捉光照特征及光照依赖型图像噪声的能力。
原文摘要 · Abstract (English)
The development of safe and robust autonomous driving functions is heavily dependent on large-scale, high-quality sensor data. However, real-world data acquisition requires extensive human labor and is strongly limited by factors such as labeling cost, driver safety protocols and scenario coverage. Thus, multiple lines of work focus on the conditional generation of synthetic camera sensor data. We identify a significant gap in research regarding daytime variation, presumably caused by the scarcity of available labels. Consequently, we present solar altitude as global conditioning variable. It is readily computable from latitude-longitude coordinates and local time, eliminating the need for manual labeling. Our work is complemented by a tailored normalization approach, targeting the sensitivity of daylight towards small numeric changes in altitude. We demonstrate its ability to accurately capture lighting characteristics and illumination-dependent image noise in the context of diffusion models.
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