上下文引导的语义标注和3D点云搜索
Contextually Guided Semantic Labeling and Search for 3D Point Clouds
摘要：
RGB-D相机，它提供RGB图像与深度，正在成为机器人感知越来越受欢迎。在本文中，我们解决的任务，检测常见的物体在三维点云的室内场景从这样的相机。我们的方法使用了一个图形模型，捕捉各种功能和上下文关系，包括当地的视觉外观和形状线索，对象共现关系和几何关系。随着大量的对象类和关系，模型的简约性变得重要，我们通过使用多种类型的边缘电位来解决这个问题。我们使用最大余量学习方法训练模型。在我们的实验中，共有52个家庭和办公室的3D场景（由约550个视图组成），我们得到了84.06%和73.38%的性能分别标记办公室和家庭场景17个对象类。我们还提出了一种方法，机器人搜索一个对象使用学习模型和上下文信息可从当前的标签的场景。我们成功地应用该算法在移动机器人上找到12个对象类在10个不同的办公室的任务，并取得了97.56%的准确率与78.43%的召回。
英文摘要：
RGB-D cameras, which give an RGB image to- gether with depths, are becoming increasingly popular for robotic perception. In this paper, we address the task of detecting commonly found objects in the 3D point cloud of indoor scenes obtained from such cameras. Our method uses a graphical model that captures various features and contextual relations, including the local visual appearance and shape cues, object co-occurence relationships and geometric relationships. With a large number of object classes and relations, the model's parsimony becomes important and we address that by using multiple types of edge potentials. We train the model using a maximum-margin learning approach. In our experiments over a total of 52 3D scenes of homes and offices (composed from about 550 views), we get a performance of 84.06% and 73.38% in labeling office and home scenes respectively for 17 object classes each. We also present a method for a robot to search for an object using the learned model and the contextual information available from the current labelings of the scene. We applied this algorithm successfully on a mobile robot for the task of finding 12 object classes in 10 different offices and achieved a precision of 97.56% with 78.43% recall.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
