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分段平面立体扫描:使用平面基元的序列结构与运动

Piecewise-Planar StereoScan: Sequential Structure and Motion Using Plane Primitives.

作者信息

Raposo Carolina, Antunes Michel, Barreto Jo Ao P

出版信息

IEEE Trans Pattern Anal Mach Intell. 2018 Aug;40(8):1918-1931. doi: 10.1109/TPAMI.2017.2737425. Epub 2017 Aug 9.

DOI:10.1109/TPAMI.2017.2737425
PMID:28796609
Abstract

The article describes a pipeline that receives as input a sequence of stereo images, and outputs the camera motion and a Piecewise-Planar Reconstruction (PPR) of the scene. The pipeline, named Piecewise-Planar StereoScan (PPSS), works as follows: the planes in the scene are detected for each stereo view using semi-dense depth estimation; the relative pose is computed by a new closed-form minimal algorithm that only uses point correspondences whenever plane detections do not fully constrain the motion; the camera motion and the PPR are jointly refined by alternating between discrete optimization and continuous bundle adjustment; and, finally, the detected 3D planes are segmented in images using a new framework that handles low texture and visibility issues. PPSS is extensively validated in indoor and outdoor datasets, and benchmarked against two popular point-based SfM pipelines. The experiments confirm that plane-based visual odometry is resilient to situations of small image overlap, poor texture, specularity, and perceptual aliasing where the fast LIBVISO2 [1] pipeline fails. The comparison against VisualSfM+CMVS/PMVS [2] , [3] shows that, for a similar computational complexity, PPSS is more accurate and provides much more compelling and visually pleasant 3D models. These results strongly suggest that plane primitives are an advantageous alternative to point correspondences for applications of SfM and 3D reconstruction in man-made environments.

摘要

本文描述了一种流水线,它接收立体图像序列作为输入,并输出相机运动和场景的分段平面重建(PPR)。该流水线名为分段平面立体扫描(PPSS),其工作方式如下:使用半密集深度估计为每个立体视图检测场景中的平面;通过一种新的闭式最小算法计算相对位姿,该算法在平面检测不能完全约束运动时仅使用点对应关系;通过在离散优化和连续光束平差之间交替来联合优化相机运动和PPR;最后,使用一个处理低纹理和可见性问题的新框架在图像中对检测到的3D平面进行分割。PPSS在室内和室外数据集中得到了广泛验证,并与两种流行的基于点的结构从运动(SfM)流水线进行了基准测试。实验证实,基于平面的视觉里程计对于小图像重叠、低纹理、镜面反射和感知混叠等情况具有鲁棒性,而快速的LIBVISO2 [1]流水线在这些情况下会失败。与VisualSfM+CMVS/PMVS [2]、[3]的比较表明,在计算复杂度相似的情况下,PPSS更准确,并且能提供更具吸引力和视觉效果更好的3D模型。这些结果有力地表明,对于人造环境中的SfM和3D重建应用,平面基元是点对应关系的一个有利替代方案。

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