基于物体面对应的RGB-D图像拼接优化方法

RGB-D image mosaic optimization method based on object-face correspondence

针对RGB-D中深度图像分辨率低、范围小、噪声大而不利于三维重建的问题,研究了一种基于物体面对应的RGB-D图像拼接优化方法。先对RGB-D图像进行预处理对齐,使用基于特征匹配算法对特征点提取和粗匹配,其次通过本文研究的不同视角下同一物体面对应关系来剔除误匹配,最后根据单应矩阵得到宽视角的RGB-D图像以及三维模型。本文使用了尺度不变特征变换(SIFT)、加速稳健特征(SURF)和定向FAST和旋转BRIEF (ORB)3种算法来进行对比实验。实验结果表明,添加本文方法后的算法在有形变、旋转的图像上分别剔除41%、29%和52%的误匹配,均方根误差减少了5%、27%和33%。在缩放的图像上分别剔除53%、57%和51%的误匹配,均方根误差减少了14%、17%和28%,提高了匹配精度,验证了本文方法的可行性。

An object-face based RGB-D image stitching optimization method is studied to solve the problem of low resolution, small range, and high noise of RGB-D depth images, which is not conducive to three-dimensional reconstruction. First, the RGB-D images are pre-processed and aligned;the feature points are extracted and roughly matched using the algorithm. Then, the mismatching is eliminated by the corresponding relationship of the same object face under different perspectives studied in this paper. Finally, the RGB-D images with wide viewing angles and three-dimensional models are obtained based on the homography matrix. Three algorithms, Scale-Invariant Feature Transform(SIFT), Speeded Up Robust Features(SURF), and Oriented FAST and Rotated BRIEF(ORB), are used for comparison experiments. The experimental results show that 41%, 29%, and 52% erroneous matches are removed on distorted and revolved images, and the Root Mean Square Error is reduced by 5%, 27%, and 33% respectively. In the scaled image, 53%, 57%, and 51% erroneous matches are removed, and the Root Mean Square Error is reduced by 14%, 17%, and 28%, which improves the matching accuracy and verifies the feasibility of this method.