  The wheel of the train is a 3D revolving geometrical structure.
Reconstructing the normal section profile is an effective approach to determine
the critical geometric parameter and wear of the wheel in the community of
railway safety. The existing reconstruction methods typically require a sensor
working in a constrained position and pose, suffering poor flexibility and
limited viewangle. This paper proposes a pose-unconstrained normal section
profile reconstruction framework for 3D revolving structures via multiple 3D
general section profiles acquired by a multi-line structured light vision
sensor. First, we establish a model to estimate the axis of 3D revolving
geometrical structure and the normal section profile using corresponding
points. Then, we embed the model into an iterative algorithm to optimize the
corresponding points and finally reconstruct the accurate normal section
profile. We conducted real experiment on reconstructing the normal section
profile of a 3D wheel. The results demonstrate that our algorithm reaches the
mean precision of 0.068mm and good repeatability with the STD of 0.007mm. It is
also robust to varying pose variations of the sensor. Our proposed framework
and models are generalized to any 3D wheeltype revolving components.
