To address the efficiency problem in batch 3D reconstruction of fixed workpieces in industrial sites, a registration optimization method integrating binocular fringe structured light, a high-precision turntable and a scanning path planning strategy is proposed to solve the problems of low efficiency, redundant scanning time and precision degradation caused by the insufficient overlap rate of traditional scanning methods. The method eliminates the interference of initial pose differences on registration results by presetting the fixed position of the workpieces, which achieves the efficient coarse registration of multi-view point clouds with the high-precision turntable and completes the fine registration by iterative closest point (ICP) algorithm. Firstly, a 3D point cloud model of measured workpieces and tri-pyramid model of binocular fringe structured light scanner are constructed, and the system parameter calibration is completed to improve the registration accuracy and path planning. Then, the mapping relationship between the turntable rotation angle and the scanning field of view is established. The ray casting method is used to simulate the projection of real light onto the surface of target point cloud, accurately calculate the visible point cloud, and compute the effective scanning area under different angular poses. Finally, based on this mapping relationship, the minimum number of scans required to complete the full 3D reconstruction of workpieces with guaranteed overlap rate and the corresponding optimal rotation angle are solved to realize scanning path optimization. Compared with the traditional uniform rotation point cloud scanning method, this method shortens the average multi-view registration time of workpiece1 to 24.2 s, improves the efficiency by about 43%, reduces the number of scans by 3, and achieves an average error of 0.011 4 mm with a precision improvement of 64.64%. For workpiece 2, the average registration time is reduced to 58.2 s, efficiency is improved by about 40.5%, the number of scans is reduced by 7, the average error is 0.008 2 mm, and precision is improved by 81.62%, respectively. In conclusion, this method improves the scanning efficiency while ensuring the high precision, which is suitable for the batch rapid detection of fixed-position workpieces in industrial sites.