论文标题
使用知识监督MCMC的桌面场景分析
Table-Top Scene Analysis Using Knowledge-Supervised MCMC
论文作者
论文摘要
在本文中，我们提出了一种概率方法，以生成来自6D对象姿势估计的桌面场景的抽象场景图。我们通过将这些知识作为马尔可夫逻辑网络中的描述性规则编码来明确利用任务特定的上下文知识。我们生成场景图的方法是概率：对象姿势中的不确定性是由嵌入数据驱动的MCMC过程中的概率传感器模型来解决的。我们将Markov Logic推论应用于有关隐藏对象的理由并检测对象姿势的错误估计。在现实世界实验中，我们证明和评估了我们方法的有效性。
In this paper, we propose a probabilistic method to generate abstract scene graphs for table-top scenes from 6D object pose estimates. We explicitly make use of task-specfic context knowledge by encoding this knowledge as descriptive rules in Markov logic networks. Our approach to generate scene graphs is probabilistic: Uncertainty in the object poses is addressed by a probabilistic sensor model that is embedded in a data driven MCMC process. We apply Markov logic inference to reason about hidden objects and to detect false estimates of object poses. The effectiveness of our approach is demonstrated and evaluated in real world experiments.
