水下目标三维点云生成优化方法

Optimization Method of 3D Point Cloud Generation for Underwater Target

由于水下光衰严重、能见度低,导致ROV作业时采集的水下图像特征难以提取,重建的水下目标三维点云辨识质量低。本文提出一种改进SIFT尺度空间的水下目标三维点云生成优化方法,该方法增强了特征提取时颜色边缘特征信息的感知,提高了有效特征点匹配数。基于ROV拍摄的水下数据集实验结果表明,相比于SIFT传统算法,该方法图像特征点数量增加2.9倍,匹配数量增加1.78倍,点云数量增加1.04倍,三维重构模型效果显著。因此该方法可在一定程度解决水下图像特征点难检测及生成的三维模型质量差的问题。

Due to the serious underwater light attenuation and low visibility, it is difficult to extract the un-derwater image features collected during ROV operation, and the recognition quality of the reconstructed underwater target 3D point cloud is low. In this paper, an optimization method for underwater target 3D point cloud generation based on improved SIFT scale space is proposed. This method enhances the perception of color edge feature information and improves the number of effective feature points. The experimental results of underwater dataset taken based on ROV show that compared with the traditional SIFT algorithm, the number of image feature points, matching and point clouds are increased by 2.9 times, 1.78 times and 1.04 times respectively. The effect of 3D reconstruction model is remarkable. Therefore, this method can solve the problem of difficult detection of underwater image feature points and poor quality of 3D model to a certain extent.