The structured light 3D measurement technology based on laser galvanometer has been widely used in industrial scenarios such as robot grasping, handling, and loading/unloading in welding and assembly workshops. However, in actual measurement scenarios, there are complex measurement structures such as depressions, overlaps, and occlusions. Light is prone to multiple reflections between micro-faces, causing intensity information to be mixed within the micro-face area and ultimately resulting in point cloud loss in this measurement area. To address the issue of point cloud loss in complex structure areas in the measurement process and ensure the accuracy of the measurement information provided by vision, a binocular point cloud sensor with a laser galvanometer as the key projection module is proposed in this work. Without adding hardware, it realizes two different image projection modes to deal with complex measurement situations within the scene. Among them, the anti-multiple reflection projection mode proposed in this work, by regulating the timing coordination relationship between key components, completes the measurement of complex structure positions and solves the problem of point cloud loss caused by multiple reflection interference. Finally, multiple experiments are conducted in actual scenarios to verify the feasibility of the proposed strategy. The experimental results show that in measurement scenarios with multiple reflection interference, the integrity of the black part point cloud measured by the anti-multiple reflection projection mode proposed in this work reaches 98.03%, which is 18.98% higher than the traditional measurement mode. It effectively solves the problem of point cloud loss in measurement scenarios with multiple reflection interference. Visual measurements ensure the accuracy and completeness of the information obtained. The six-axis compensation values determined by the robot for each part’s pose state during teaching become more precise. This ensures that the previously taught robot trajectory can be accurately reused for subsequent poses, thereby reducing the time needed for manual robot debugging and enhancing production efficiency.