Airborne laser scanning allows for efficient acquisition of accurate 3D data for large areas. Because georeferencing of the LiDAR data requires knowledge of the platform trajectory, the laser scanner system commonly comprises a global navigation satellite system (GNSS) receiver/antenna and an inertial measurement unit (IMU). The standard processing pipeline consists of GNSS/IMU integration, georeferencing, and subsequent adjustment of the laser data. Here, we consider a holistic GNSS/IMU/LiDAR-integration approach based on least-squares adjustment. The GNSS is loosely coupled, and the GNSS positions are obtained using either postprocessing kinematic or precise point positioning GNSS processing strategies using the open-source software RTKLib. In this contribution, we compare the resulting point clouds to those of a standard processing workflow and evaluate the impact of the different processing strategies on point cloud quality in terms of internal consistency and absolute accuracy for a airborne laser bathymetry (ALB) dataset. Although the GNSS solutions themselves differ strongly, both the PPK- and the PPP-derived point clouds show better strip differences (below 2.5 cm) and similar absolute accuracy (<4 cm RMSE w.r.t. reference targets after correction of constant datum shift) compared to the reference solution.