提出新指标衡量仿真与真实图像的感知性能差距，助力机器人视觉真实迁移。
Instance Performance Difference: A Metric to Measure the Sim-To-Real Gap in Camera Simulation
- 通过配对真实与仿真图像，用感知算法评估性能相似性。
- 在月球地形岩石检测任务中验证，能准确识别最接近真实的合成方法。
- 适合需要高保真仿真数据的机器人感知系统开发人员使用。
本文提出实例性能差异（IPD）这一新指标，用于衡量机器人感知任务在真实图像与合成图像上的性能差距。通过将合成图像与真实图像中的对应实例配对，并利用感知算法评估其性能相似性，IPD 能精准反映仿真与现实之间的差距。我们以月球地形图像中的岩石检测任务为例，展示了该指标在识别最逼真图像生成方法方面的有效性。该指标有助于构建与真实照片表现一致的合成图像数据集，从而支持感知算法在真实机器人应用中的稳健仿真到现实迁移。
原文摘要 · Abstract (English)
In this contribution, we introduce the concept of Instance Performance Difference (IPD), a metric designed to measure the gap in performance that a robotics perception task experiences when working with real vs. synthetic pictures. By pairing synthetic and real instances in the pictures and evaluating their performance similarity using perception algorithms, IPD provides a targeted metric that closely aligns with the needs of real-world applications. We explain and demonstrate this metric through a rock detection task in lunar terrain images, highlighting the IPD's effectiveness in identifying the most realistic image synthesis method. The metric is thus instrumental in creating synthetic image datasets that perform in perception tasks like real-world photo counterparts. In turn, this supports robust sim-to-real transfer for perception algorithms in real-world robotics applications.
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