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首页> 外文期刊>IEEE Transactions on Pattern Analysis and Machine Intelligence >The Manhattan Frame Model—Manhattan World Inference in the Space of Surface Normals
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The Manhattan Frame Model—Manhattan World Inference in the Space of Surface Normals

机译：曼哈顿框架模型-曲面法线空间中的曼哈顿世界推断

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摘要

Objects and structures within man-made environments typically exhibit a high degree of organization in the form of orthogonal and parallel planes. Traditional approaches utilize these regularities via the restrictive, and rather local, Manhattan World (MW) assumption which posits that every plane is perpendicular to one of the axes of a single coordinate system. The aforementioned regularities are especially evident in the surface normal distribution of a scene where they manifest as orthogonally-coupled clusters. This motivates the introduction of the Manhattan-Frame (MF) model which captures the notion of an MW in the surface normals space, the unit sphere, and two probabilistic MF models over this space. First, for a single MF we propose novel real-time MAP inference algorithms, evaluate their performance and their use in drift-free rotation estimation. Second, to capture the complexity of real-world scenes at a global scale, we extend the MF model to a probabilistic mixture of Manhattan Frames (MMF). For MMF inference we propose a simple MAP inference algorithm and an adaptive Markov-Chain Monte-Carlo sampling algorithm with Metropolis-Hastings split/merge moves that let us infer the unknown number of mixture components. We demonstrate the versatility of the MMF model and inference algorithm across several scales of man-made environments.
机译：人造环境中的物体和结构通常以正交和平行平面的形式表现出高度的组织性。传统方法通过限制性（而不是局部性）的曼哈顿世界（MW）假设来利用这些规则，该假设假定每个平面都垂直于单个坐标系的轴之一。前述规则在场景的表面正态分布中特别明显，在这些场景中，它们表现为正交耦合的群集。这激发了曼哈顿框架（MF）模型的引入，该模型捕获了表面法线空间，单位球体内的MW概念以及该空间上的两个概率MF模型。首先，对于单个MF，我们提出了新颖的实时MAP推理算法，评估了它们的性能以及它们在无漂移旋转估计中的使用。其次，为了在全球范围内捕获现实世界场景的复杂性，我们将MF模型扩展为曼哈顿框架（MMF）的概率混合。对于MMF推论，我们提出了一种简单的MAP推论算法和一种具有Metropolis-Hastings分裂/合并移动的自适应Markov-Chain蒙特卡洛采样算法，可以推断未知数量的混合组分。我们展示了MMF模型和推理算法在多种规模的人造环境中的多功能性。

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