快速鲁棒的城市场景分段平面重建

对于基于图像的城市场景重建，由于光照变化、透视畸变、弱纹理区域等因素的影响，传统像素级与区域级的重建算法通常难以获得可靠的重建结果.为了解决此问题，本文提出一种快速、鲁棒的分段平面重建算法.根据城市场景结构特征与分段平面假设，本文算法首先利用基于连通域检测的空间平面拟合方法从初始空间点中抽取充分且可靠的候选空间平面，然后在MRF（Markov random field）能量最小化框架下将场景的完整结构推断问题转化为平面标记问题进行求解.由于候选平面集与融合灰度一致性度量、空间几何与可见性约束的能量模型的高可靠性，场景的完整结构因此可被有效地重建.实验结果表明，本文算法能较好地克服传统算法可靠性差、重建场景不完整等缺点，同时具有较高的计算效率.

Rapid and Robust Piecewise Planar Reconstruction of Urban Scenes

For image-based urban scene reconstruction, traditional pixel-level or region-level methods often fail to achieve satisfactory results because of various negative factors such as illumination variations, perspective distortion, poorly textured regions, etc. To address this problem, a rapid and robust piecewise planar stereo method is proposed in this paper. Under the scene piecewise planar assumption and by taking into account the structural characteristics of urban scenes, the proposed method at first extracts sufficient and reliable candidate planes from initial spatial points based on the connected-region detection; then the problem of scene reconstruction is converted into a plane labeling problem under the Markov random field (MRF) framework. By virtue of the high reliability of our extracted candidate planes and the energy model properly designed by incorporating geometric constraints, spatial visibility and photo-consistency measures, a complete scene structure is effectively reconstructed. Experiment results show that the proposed method can satisfactorily handle the low-reliability and incomplete-reconstruction problems in traditional methods with computational efficiency.