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Piecewise-planar reconstruction using two views

机译:使用两个视图的分段平面重建

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The article describes a reconstruction pipeline that generates piecewise-planar models of man-made environments using two calibrated views. The 3D space is sampled by a set of virtual cut planes that intersect the baseline of the stereo rig and implicitly define possible pixel correspondences across views. The likelihood of these correspondences being true matches is measured using signal symmetry analysis [1], which enables to obtain profile contours of the 3D scene that become lines whenever the virtual cut planes intersect planar surfaces. The detection and estimation of these lines cuts is formulated as a global optimization problem over the symmetry matching cost, and pairs of reconstructed lines are used to generate plane hypotheses that serve as input to PEARL clustering [2]. The PEARL algorithm alternates between a discrete optimization step, which merges planar surface hypotheses and discards detections with poor support, and a continuous optimization step, which refines the plane poses taking into account surface slant. The pipeline outputs an accurate semi-dense Piecewise-Planar Reconstruction of the 3D scene. In addition, the input images can be segmented into piecewise-planar regions using a standard labeling formulation for assigning pixels to plane detections. Extensive experiments with both indoor and outdoor stereo pairs show significant improvements over state-of-the-art methods with respect to accuracy and robustness. (C) 2016 Elsevier B.V. All rights reserved.
机译:本文介绍了一种重建管道,该管道使用两个校准后的视图生成人造环境的分段平面模型。 3D空间由一组虚拟切面进行采样,这些切面与立体模型的基线相交,并隐式定义了视图之间可能存在的像素对应关系。这些对应关系是真实匹配的可能性是使用信号对称分析[1]进行测量的,该信号对称分析可以获取3D场景的轮廓轮廓,每当虚拟切割平面与平面相交时,轮廓轮廓就会变成线条。这些线切割的检测和估计被公式化为对称匹配成本之上的全局优化问题,并且使用成对的重构线来生成平面假设,以作为PEARL聚类的输入[2]。 PEARL算法在离散优化步骤和连续优化步骤之间交替进行,该优化步骤合并了平面假设并丢弃了支撑不佳的检测结果,而该连续优化步骤则考虑了表面倾斜来优化了平面姿势。管线输出3D场景的精确半密集分段平面重建。另外,可以使用标准标记配方将输入图像分割成分段平面区域,以将像素分配给平面检测。室内和室外立体声对的大量实验表明,相对于最新技术,其准确性和鲁棒性得到了显着提高。 (C)2016 Elsevier B.V.保留所有权利。

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