Nonparametric Bayesian Models for Unsupervised Scene Analysis and Reconstruction
Nonparametric Bayesian Models for Unsupervised Scene Analysis and Reconstruction
复制标题
用于无监督场景分析和重建的非参数贝叶斯模型
DOI：
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
Wolfram Burgard
中科院分区：
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作者：
D. Joho;Gian Diego Tipaldi;Nikolas Engelhard;C. Stachniss;Wolfram Burgard
在国内环境中运行的机器人需要处理各种不同的对象。通常，这些对象既不是随机放置，也不是彼此独立的。例如，早餐桌上的物体（例如盘子，刀具或碗）通常以经常性配置出现。在本文中，我们提出了一个新颖的层次生成模型，以推理场景中潜在对象星座。所提出的模型是Dirichlet过程和β过程的组合，该过程允许对参数空间的未知维度进行概率处理。我们展示了如何使用模型来解决场景中的一组不同任务，从无监督的场景细分到完成部分指定的场景。我们描述了如何使用Markov链蒙特卡洛（MCMC）技术进行此模型中的采样，并通过使用Kinect摄像机获得的模拟和现实数据进行了实验评估。
Robots operating in domestic environments need to deal with a variety of different objects. Often, these objects are neither placed randomly, nor independently of each other. For example, objects on a breakfast table such as plates, knives, or bowls typically occur in recurrent configurations. In this paper, we propose a novel hierarchical generative model to reason about latent object constellations in a scene. The proposed model is a combination of Dirichlet processes and beta processes, which allow for a probabilistic treatment of the unknown dimensionality of the parameter space. We show how the model can be employed to address a set of different tasks in scene understanding ranging from unsupervised scene segmentation to completion of a partially specified scene. We describe how sampling in this model can be done using Markov chain Monte Carlo (MCMC) techniques and present an experimental evaluation with simulated as well as real-world data obtained with a Kinect camera.