点云
人工智能
计算机科学
投影(关系代数)
单发
帧速率
计算机视觉
帧(网络)
点(几何)
结构光三维扫描仪
轮廓仪
弹丸
一次性
光学
算法
数学
物理
材料科学
扫描仪
工程类
表面粗糙度
冶金
电信
几何学
量子力学
机械工程
作者
Ruizhi Zuo,Shuwen Wei,Yaning Wang,Michael Kam,Justin D. Opfermann,Michael H. Hsieh,Axel Krieger,Jin U. Kang
摘要
Real-time fringe projection profilometry (FPP) is developed as a 3D vision system to plan and guide autonomous robotic intestinal suturing. Conventional FPP requires sinusoidal patterns with multiple frequencies, and phase shifts to generate tissue point clouds, resulting in a slow frame rate. Therefore, although FPP can reconstruct dense and accurate tissue point clouds, it is often too slow for dynamic measurements. To address this problem, we propose a deep learning-based single-shot FPP algorithm, which reconstructs tissue point clouds with a single sinusoidal pattern using a Swin-Unet. With this approach, we have achieved a FPP imaging frame rate of 50Hz while maintaining high point cloud measurement accuracy. System performance was trained and evaluated both by synthesized and an experimental dataset. An overall relative error of 1~3% was achieved.
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