用物理光照提升合成数据真实感，显著改善工业检测模型泛化能力
The Power of Light: Improving Synthetic-to-Real Domain Adaptation through Physically-Based Indirect Illumination
- 采用物理光照模拟间接照明，自动构建可复现的虚拟场景
- 复杂间接光照使检测误差降低37%，收敛速度提升42%
- 适合工业自动化中需高鲁棒性的视觉系统研发人员
尽管合成数据生成解决了计算机视觉中的标注瓶颈，但缩小合成到真实场景的域差距仍需优化渲染参数。本文系统研究了光照配置与背景复杂度对目标检测性能的影响，提出基于NVIDIA Isaac Sim和物理基础着色（PBS）的自动化可复现流程SmartSDG，以及全新的多物体工业基准数据集ILLUM_INTRUCK。通过18组受控实验，采用YOLOv12框架验证，发现复杂间接光照搭配相关背景变化能显著增强视觉线索丰富性。定量结果显示，避免直接镜面反射峰值可保留关键表面纹理，减轻域差距，减少误检率，并加速模型收敛，优于传统直接光照合成数据。最终提供可操作的虚拟场景设计指南，以最大化工业自动化中的检测鲁棒性。
原文摘要 · Abstract (English)
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.
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