We present a novel method to estimate the motion matrix between overlapping pairs of 3D views in the context of indoor scenes. We use the Manhattan world assumption to introduce lightweight geometric constraints under the form of planes into the problem, which reduces complexity by taking into account the structure of the scene. In particular, we define a stochastic framework to categorize planes as vertical or horizontal and parallel or non-parallel. We leverage this classification to match pairs of planes in overlapping views with point-of-view agnostic structural metrics. We propose to split the motion computation using the classification and estimate separately the rotation and translation of the sensor, using a quadric minimizer. We validate our approach on a toy example and present quantitative experiments on a public RGB-D dataset, comparing against recent state-of-the-art methods. Our evaluation shows that planar constraints only add low computational overhead while improving results in precision when applied after a prior coarse estimate. We conclude by giving hints towards extensions and improvements of current results.

中文翻译：

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用于高效 3D 视图配准的平面对匹配

我们提出了一种新方法来估计室内场景中重叠的 3D 视图对之间的运动矩阵。我们使用曼哈顿世界假设将平面形式下的轻量级几何约束引入到问题中，通过考虑场景的结构来降低复杂度。特别是，我们定义了一个随机框架来将平面分类为垂直或水平以及平行或非平行。我们利用这种分类将重叠视图中的平面对与视点不可知的结构度量相匹配。我们建议使用分类拆分运动计算，并使用二次最小化器分别估计传感器的旋转和平移。我们在玩具示例上验证了我们的方法，并在公共 RGB-D 数据集上进行了定量实验，与最近最先进的方法进行比较。我们的评估表明，在先验粗略估计之后应用平面约束时，平面约束只会增加低计算开销，同时提高精度。我们最后给出了对当前结果的扩展和改进的提示。