3D surface metrology scanners are primarily used for surface reconstruction in reverse engineering and for off-line inspection of parts but, their application potential for in-line process control remains largely unexplored. The lack of usage of 3D surface metrology scanners for in-line process control can be attributed to the large processing time of metrology equipment compared to the cycle time of automotive assembly systems. To overcome this challenge, a novel methodology for adaptive measurement and modelling of data captured by in-line 3D surface measurement systems is proposed in this paper. The methodology models part deviations by augmenting the scanner data with spatio-temporal correlations in deviation field and enables efficient implementation of in-line part shape measurement for process control. The proposed methodology consists of two steps: (i) modelling deviations to enable prediction of entire part deviations with partial measurements, and; (ii) adaptively selecting part regions to be measured by taking into account the measurement data available from the scanner up to current time-step. The partial measurement strategy based on deviation modeling and adaptive region selection results in maximum information gain within the given assembly system cycle time. The prediction of entire part surface deviation with higher confidence leads to effective identification of part-to-part defect variation patterns. The proposed methodology is demonstrated on an automotive door component.