用亮度键控技术扩充了YCB物体数据集，提升目标定位训练效果。
YCB-LUMA: YCB Object Dataset with Luminance Keying for Object Localization
- 通过亮度键控法采集高精度图像，实现物体快速标注。
- 新增透明、多色、非刚性物体，数据集覆盖更广。
- 适合测试新2D检测与分割算法在复杂场景的表现。
在计算机视觉中，定位目标物体是自动驾驶、机器人、质量检测等应用的关键第一步。当前最优方案依赖深度神经网络，需大量高质量、多样化的训练数据。但数据构建成本高、易出错。亮度键控法可高效获取高质量标注数据。本文在已有YCB-V数据集基础上，扩展了完整YCB超集的其余物体，新增了透明、多色及非刚性物体，进一步验证了该方法在提升数据多样性与可用性方面的有效性，可用于评估新型2D目标检测与分割算法在复杂条件下的表现。
原文摘要 · Abstract (English)
Localizing target objects in images is an important task in computer vision. Often it is the first step towards solving a variety of applications in autonomous driving, maintenance, quality insurance, robotics, and augmented reality. Best in class solutions for this task rely on deep neural networks, which require a set of representative training data for best performance. Creating sets of sufficient quality, variety, and size is often difficult, error prone, and expensive. This is where the method of luminance keying can help: it provides a simple yet effective solution to record high quality data for training object detection and segmentation. We extend previous work that presented luminance keying on the common YCB-V set of household objects by recording the remaining objects of the YCB superset. The additional variety of objects - addition of transparency, multiple color variations, non-rigid objects - further demonstrates the usefulness of luminance keying and might be used to test the applicability of the approach on new 2D object detection and segmentation algorithms.
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