Online Reconstruction of Indoor Scenes With Local Manhattan Frame Growing
Online Reconstruction of Indoor Scenes With Local Manhattan Frame Growing
复制标题
曼哈顿局部框架生长的室内场景在线重建
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
W. Sheng
中科院分区：
文献类型：
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作者：
Mahdi Yazdanpour;Guoliang Fan;W. Sheng
提出了一种基于连续曼哈顿关键帧间几何关系和局部姿态细化的室内场景鲁棒重建方法，以提高重建模型的精度和逼真度。在我们的框架的核心，我们有一个本地曼哈顿帧生长系统，它找到场景的主要方向，并对齐点云与主平面，和一个本地姿势优化，它细化了特定范围的帧的姿势估计。在重建过程中，我们使用曼哈顿关键帧进行平面预对准，为最终的表面配准提供鲁棒的初始化。所有的曼哈顿关键帧集成使用帧到模型的计划，以创建本地模型的基础上，完善的相机姿势。通过局部区域间的几何配准并将其整合到一个全局框架中，重建出最终的稠密模型。实验结果表明，我们的方法，以减少累积配准误差和整体几何漂移的有效性。
We propose an efficient approach for robust reconstruction of indoor scenes by taking advantage of the geometric relation between consecutive Manhattan keyframes and local pose refinement to improve the accuracy and fidelity of the reconstructed models. At the core of our framework, we have a Local Manhattan Frame Growing system, which finds the principal directions of the scene and aligns point clouds with the dominant plane, and a Local Pose Optimization, which refines the pose estimation for a specific range of frames. During the reconstruction process, we use Manhattan keyframes for a planar pre-alignment to provide a robust initialization for the final surface registration. All Manhattan keyframes are integrated using a frame-to-model scheme to create local models based on the refined camera poses. The final dense model is reconstructed by adopting a geometric registration between local segments and integrating them into a global frame. The experimental results show the effectiveness of our approach to reduce the cumulative registration error and overall geometric drift.
影响因子：
6.2
作者：
Niessner, Matthias;Zollhoefer, Michael;Stamminger, Marc
通讯作者：
Stamminger, Marc