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Reconstructing normal section profiles is an effective approach at monitoring the shape of a 3-D revolving-symmetry structure. The existing reconstruction methods typically require a sensor to work in a constrained pose and thus suffer from poor flexibility and limited view-angle. This article proposes a pose-unconstrained normal section profile reconstruction framework for 3-D revolving-symmetry structures, which estimates the normal section profile using multiple 3-D general section profiles acquired by a multiline structured light vision sensor. First, we establish a model to estimate the revolving axis and calculate the normal section profile using corresponding points. Then, the model is embedded into an iterative algorithm to optimize the corresponding points and calculate the accurate normal section profile. Simulations show that our algorithm is applicable to 3-D revolving structures of various shapes and is robust against noise and local surface defects. Real experiments were conducted on reconstructing the normal section profile of a 3-D wheel. The results demonstrate that our algorithm reaches the mean precision of 0.065 mm and repeatability with the STD of 0.007 mm. It is also robust to pose and position of the sensor.

Reconstructing Normal Section Profiles of 3-D Revolving Structures via Pose-Unconstrained Multiline Structured-Light Vision