基于四叉树先验辅助的多视图立体方法

Multi-view stereo method based on quadtree prior assistance

基于PatchMatch的多视图立体(MVS)方法依据输入多幅图像估计场景的深度,目前已应用于大规模场景三维重建。然而,由于特征匹配不稳定、仅依赖光度一致性不可靠等原因,现有方法在弱纹理区域的深度估计准确性和完整性较低。针对上述问题,提出一种基于四叉树先验辅助的MVS方法。首先,利用图像像素值获得局部纹理;其次,基于自适应棋盘网格采样的块匹配多视图立体视觉方法(ACMH)获得粗略的深度图,结合弱纹理区域中的结构信息,采用四叉树分割生成先验平面假设;再次,融合上述信息,设计一种新的多视图匹配代价函数,引导弱纹理区域得到最优深度假设,进而提高立体匹配的准确性;最后,在ETH3D、Tanks and Temples和中国科学院古建筑数据集上与多种现有的传统MVS方法进行对比实验。结果表明所提方法性能更优,特别是在ETH3D测试数据集中,当误差阈值为2 cm时,相较于当前先进的多尺度平面先验辅助方法(ACMMP),它的F1分数和完整性分别提高了1.29和2.38个百分点。

PatchMatch-based Multi-View Stereo(MVS)method can estimate the depth of a scene based on multiple input images and is currently applied in large-scale 3D scene reconstruction.However,the existing methods have lower accuracy and completeness in depth estimation in low-texture regions due to unstable feature matching and unreliable reliance on photometric consistency alone.To address the above problems,an MVS method based on quadtree prior assistance was proposed.Firstly,the image pixel values were used to obtain local textures.Secondly,a coarse depth map was obtained by Adaptive Checkerboard sampling and Multi-Hypothesis joint view selection(ACMH),which combined the structural information in the low-texture region to generate a priori plane hypothesis by using quadtree segmentation.Thirdly,by integrating the above information,a new multi-view matching cost function was designed to guide the low-texture regions for obtaining the best depth assumption,thereby improving the accuracy of stereo matching.Finally,comparison experiments were conducted with many existing traditional MVS methods on ETH3D,Tanks and Temples,and Chinese Academy of Sciencesancient architecture datasets.The results demonstrate that the proposed method performs better,especially in ETH3D test dataset with error threshold of 2 cm,its F1 score and completeness are improved by 1.29 and 2.38 percentage points,respectively,compared with the current state-of-the-art multi-scale geometric consistency guided and planar prior assisted multi-view stereo method(ACMMP).