想提升合成数据训练的检测模型效果？这篇论文用YOLOv12做了18组实验，证明间接光照比直射光更靠谱，还给了具体设计建议。搞工业视觉的一定要看。
该论文系统研究了光照配置和背景复杂度对物体检测性能的影响，提出基于NVIDIA Isaac Sim的SmartSDG自动化管道，构建了多目标工业基准数据集ILLUM_INTRUCK。通过18个使用YOLOv12框架的控制实验，发现复杂间接光照搭配域相关背景能显著提升视觉线索丰富度。避免直接镜面峰值可保留表面纹理、缩小域差距并降低误报，加速模型收敛。最终提供了面向工业自动化的虚拟场景设计指南。
The Power of Light: Improving Synthetic-to-Real Domain Adaptation through Physically-Based Indirect Illumination
While synthetic data generation resolves the manual labeling bottleneck in computer vision, minimizing the syn-to-real domain gap requires optimizing rendering variables. This paper presents a systematic study analyzing the impact of lighting configurations and background complexity on object detection performance. We introduce SmartSDG, an automated, reproducible pipeline built on NVIDIA Isaac Sim using Physically-Based Shading (PBS), alongside ILLUM\_INTRUCK, a new multi-object industrial benchmark dataset. Through 18 controlled experiments utilizing a state-of-the-art YOLOv12 framework, we demonstrate that complex, indirect lighting configurations paired with domain-relevant background variability significantly increase visual cue richness. Our quantitative findings show that avoiding direct specular peaks preserves crucial surface textures, mitigates the domain gap, reduces false positives, and accelerates model convergence compared to using conventional direct-light synthetic data. Ultimately, we provide actionable virtual scene design guidelines to maximize object detection robustness in industrial automation.