针对室内小场景下图像采集位置受限、弱纹理物体重建效果不佳的问题,提出了一种只需用手机采集图像的三维重建算法。首先,用一种主动选择式图像匹配策略减少原始运动恢复结构算法中图像两两匹配的次数。然后,将尺度不变特征变换(SIFT)算法改进为Harris-SIFT算法,以提升算法的实时性;通过全卷积神经网络获得预测深度并与多视图立体匹配算法进行融合,以获得更多的稠密点云。最后,用泊松表面重建算法完成物体的重建。实验结果表明,本算法不仅能有效恢复室内场景下的物体细节特征,对弱纹理物体表面的重建效果也较好。相比原始算法,本算法所用的时间减少了21.07%。

In this paper, aiming at the problem of limited image acquisition location and poor reconstruction of weak texture objects in small indoor scenes, a three-dimensional reconstruction algorithm that only needs a mobile phone to acquire images is proposed. First, an active selective image matching policy is employed to reduce the number of images of pairwise matching in the original structure from motion algorithm. Then, the scale-invariant feature transform (SIFT) algorithm is improved to the Harris-SIFT algorithm to enhance real-time performance of the algorithm. Next, the predicted depth is obtained from the full consolidation neural network and fused with a multi-view stereo match algorithm to obtain more dense clouds. Finally, the reconstruction of the object is completed with a Poisson surface reconstruction algorithm. The experiment results show that the algorithm can not only effectively restore the detailed features of the object under the indoor scenes, but also has a better reconstruction effect on the surface of the weak texture objects. Compared with the original reconstruction algorithm, the time used by the algorithm is reduced by 21.07%.