论文标题
自我监督的透明液体分割，用于机器人浇注
Self-supervised Transparent Liquid Segmentation for Robotic Pouring
论文作者
论文摘要
液态估计对于诸如浇注之类的机器人技术任务很重要。但是，估计透明液体的状态是一个具有挑战性的问题。我们提出了一种新型的分割管道，该管道可以从静态的RGB图像中分割透明液体，例如水，而无需任何手动注释或加热液体进行训练。取而代之的是，我们使用能够将彩色液体图像转换为合成生成的透明液体图像的生成模型，仅在未配对的有色和透明液体图像的数据集上训练。使用背景减法自动获得有色液体的分割标签。我们的实验表明，我们能够准确预测透明液体的分割面膜，而无需任何手动注释。我们证明了在机器人浇注的任务中透明液体分割的实用性，该任务通过感知透明杯中的液体高度来控制倾泻而成。可以找到随附的视频和补充材料
Liquid state estimation is important for robotics tasks such as pouring; however, estimating the state of transparent liquids is a challenging problem. We propose a novel segmentation pipeline that can segment transparent liquids such as water from a static, RGB image without requiring any manual annotations or heating of the liquid for training. Instead, we use a generative model that is capable of translating images of colored liquids into synthetically generated transparent liquid images, trained only on an unpaired dataset of colored and transparent liquid images. Segmentation labels of colored liquids are obtained automatically using background subtraction. Our experiments show that we are able to accurately predict a segmentation mask for transparent liquids without requiring any manual annotations. We demonstrate the utility of transparent liquid segmentation in a robotic pouring task that controls pouring by perceiving the liquid height in a transparent cup. Accompanying video and supplementary materials can be found
