Exposure time and point cloud quality prediction for active 3D imaging sensors using Gaussian process regression

Setting an optimal image exposure is crucial for acquiring dense point clouds using 3D active optical sensor systems such as structured light sensors [structured light sensors (SLSs)] and active stereo sensors. One of the most common and seamless ways to optimize the image brightness of an image exposure is to adjust the camera’s exposure time. However, optimizing the image exposure alone is ineffective for acquiring surfaces of large-scale objects with a complex topology if a spatial understanding of the scene is neglected. Hence, the present paper proposes a data-driven approach using two Gaussian processes [Gaussian processes (GPs)] regression models to select a proper exposure time considering the nonlinear correlations between image exposure and the scene spatial relationships. To model these correlations, our study introduces first the generic synthesization of seven inputs and two target variables. Then, based on these inputs, two independent GPs are designed: one for predicting the measurement quality and one for estimating the exposure time. The performance and generalizability of both models are thoroughly evaluated using an SLS and an active stereo sensor. The evaluation demonstrated that the point cloud quality models adequately matched observations with an R2 exceeding 90%. Specifically, the models predicted point cloud quality with an root mean square error (RMSE) of 10%. Additionally, the assessment of the performance of the exposure time models showed a model fit with an R2 above 97%. The exposure time prediction accuracy, as evidenced by the RMSE values, was within 10% of the corresponding exposure time range for each sensor. The present research shows the potential and effectiveness to completely automate the assessment of a point cloud quality and the selection of exposure times with the help of data-driven models.