基于可变形卷积的双目视觉三维重建

提出一种基于可变形卷积的立体匹配算法来进行双目视觉三维重建。首先,采用二维可变形卷积对输入的左右两幅图像进行特征提取;然后,利用三维可变形卷积,在匹配代价空间中有效地聚合两个图像之间的相关特征;最后,采用3个阶段级联残差学习的方式来降低匹配代价空间的参数计算量,以达到快速匹配的实时要求。根据该算法原理完成了视差深度图的检测,并通过Open3D重建三维物体。实验结果表明:该算法的参数量为0.5×10⁶,运行时间只需0.02s,生成的视差图精度较高,三维重建效果较好。

Binocular Vision 3D Reconstruction Based on Deformable Convolution

A stereo matching algorithm based on deformable convolution is proposed to perform 3D reconstruction of binocular vision.Firstly, the two-dimensional deformable convolution is used to extract the features of the left and right input images.Secondly, the three-dimensional deformable convolution is used to effectively aggregate the relevant features between the two images in the matching cost volume.Finally, a three-stage cascade residual learning method is used to reduce the parameter calculation amount of the matching cost volume, which can meet the real-time requirements of fast matching.According to the principle of the algorithm, the detection of the disparity depth map is completed, and the three-dimensional object is reconstructed through Open3D.The experimental results show that the parameter amount of the algorithm is 0.5×10⁶, the running time is only 0.02s, the generated disparity map has high precision, and the reconstructed 3D effect is good.