信念场景图：通过期望计算用物体扩展部分场景
Belief Scene Graphs: Expanding Partial Scenes with Objects through Computation of Expectation
AI总结：
提出信念场景图概念，通过基于图的CECI方法计算期望并添加盲节点，扩展部分三维场景图，支持机器人高层任务规划，在物体搜索中验证有效性。
摘要：
在本文中，我们提出了信念场景图（Belief Scene Graphs）这一新颖概念，它是部分三维场景图的效用驱动扩展，能够在信息不完整的情况下实现高效的高层任务规划。我们提出了一种基于图的学习方法，用于计算任意给定三维场景图上的信念（也称为期望），该信念随后被用于策略性地添加与机器人任务相关的新节点（称为盲节点）。我们提出了基于相关性信息的期望计算方法（CECI），通过从可用训练数据中学习直方图，来合理近似真实的信念/期望。我们开发了一种新颖的图卷积神经网络（GCN）模型，用于从三维场景图库中学习CECI。由于目前不存在用于训练新颖CECI模型的三维场景图数据库，我们提出了一种基于语义标注的真实三维空间生成三维场景图数据集的新方法。生成的数据集随后被用于训练所提出的CECI模型，并对所提方法进行广泛验证。我们确立了信念场景图（BSG）这一新概念，作为将期望整合到抽象表示中的核心组件。这一新概念是经典三维场景图概念的演进，旨在实现高层推理，以支持任务规划和各种机器人任务的优化。整体框架的有效性已在物体搜索场景中进行了评估，并在真实生活实验中进行了测试，以模拟人类对未见物体的常识。有关展示实验演示的文章视频，请参考以下链接：https://youtu.be/hsGlSCa12iY
英文摘要：
In this article, we propose the novel concept of Belief Scene Graphs, which are utility-driven extensions of partial 3D scene graphs, that enable efficient high-level task planning with partial information. We propose a graph-based learning methodology for the computation of belief (also referred to as expectation) on any given 3D scene graph, which is then used to strategically add new nodes (referred to as blind nodes) that are relevant to a robotic mission. We propose the method of Computation of Expectation based on Correlation Information (CECI), to reasonably approximate real Belief/Expectation, by learning histograms from available training data. A novel Graph Convolutional Neural Network (GCN) model is developed, to learn CECI from a repository of 3D scene graphs. As no database of 3D scene graphs exists for the training of the novel CECI model, we present a novel methodology for generating a 3D scene graph dataset based on semantically annotated real-life 3D spaces. The generated dataset is then utilized to train the proposed CECI model and for extensive validation of the proposed method. We establish the novel concept of \textit{Belief Scene Graphs} (BSG), as a core component to integrate expectations into abstract representations. This new concept is an evolution of the classical 3D scene graph concept and aims to enable high-level reasoning for task planning and optimization of a variety of robotics missions. The efficacy of the overall framework has been evaluated in an object search scenario, and has also been tested in a real-life experiment to emulate human common sense of unseen-objects. For a video of the article, showcasing the experimental demonstration, please refer to the following link: https://youtu.be/hsGlSCa12iY
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。
