Dynamic BB-spline surface reconstruction: Closing the sensing-and-modeling loop in 3D digitization

In this paper, we present a new BB-spline surface reconstruction approach, called dynamic surface reconstruction, aiming to close the sensing-and-modeling loop in 3D digitization. At its core, this approach uses a recursive least squares method, the Kalman filter  , to dynamically reconstruct the BB-spline surface as the surface data are acquired. That is, the acquired data are dynamically incorporated into the surface model and the updated surface model is then used to dynamically guide further data acquisition. It thus enables a closed-loop shape sensing-and-modeling methodology for 3D digitization.Our technical contribution lies on the exploitation of the recursive nature of the Kalman filter for BB-spline surface reconstruction. This enables dynamic parameterization of data points, dynamic determination of next optimal sensing locations, and low-discrepancy based efficient sensing and reconstruction. Experiments demonstrate that such dynamic surface reconstruction leads to more efficient data acquisition and better surface reconstruction.