论文标题
浪费对象细分的多层次方法
A Multi-Level Approach to Waste Object Segmentation
论文作者
论文摘要
我们解决了从颜色图像和可选深度图像中定位废物对象的问题，这是用于与此类对象的机器人互动的关键感知组件。具体而言，我们的方法在空间粒度的多个层次上集成了强度和深度信息。首先，场景级深网络会产生初始的粗分段，基于我们选择一些潜在的对象区域来放大并执行精细的分割。上述步骤的结果进一步集成到密集连接的条件随机场中，该场学会尊重具有像素级准确性的外观，深度和空间亲和力。此外，我们创建了一个新的RGBD废物对象细分数据集Mju Waste，该数据集被公开以促进该领域的未来研究。我们方法的功效在上下文（Taco）数据集中的MJU废物和垃圾注释上得到了验证。
We address the problem of localizing waste objects from a color image and an optional depth image, which is a key perception component for robotic interaction with such objects. Specifically, our method integrates the intensity and depth information at multiple levels of spatial granularity. Firstly, a scene-level deep network produces an initial coarse segmentation, based on which we select a few potential object regions to zoom in and perform fine segmentation. The results of the above steps are further integrated into a densely connected conditional random field that learns to respect the appearance, depth, and spatial affinities with pixel-level accuracy. In addition, we create a new RGBD waste object segmentation dataset, MJU-Waste, that is made public to facilitate future research in this area. The efficacy of our method is validated on both MJU-Waste and the Trash Annotation in Context (TACO) dataset.
