One of the main challenges during digital post-processing of x-ray computed tomographic (XCT) measurement data is the reconstruction of the surface geometry of the measured objects. Conventionally, the surface geometry is defined as an isosurface of identical greyscale values, i.e. the x-ray absorbance of the material, based on a linear interpolation between neighboring voxels. Due to the complex surface geometry and rough surface, XCT measurements of additively manufactured (AM) parts are particularly prone to measurement artefacts caused by various physical effects when the x-rays penetrate the material. The irregular greyscale values at the measured surface geometry render commonly used single threshold greyscale value based isosurfaces as insufficient for representing the external and internal surface of the measured objects. This issue becomes particularly apparent when measuring multi-material objects, such as additively manufactured objects with integrated RFID tags. To address this challenge, this study presents a methodology for reliable surface geometry determination of XCT data based on previously acquired fringe projection (FP) data. For this purpose, the conventionally acquired surfaces geometries from the XCT and FP measurements are extracted, pre-processed and registered to each other before being merged into a single mesh. This merged data set is subsequently used as a starting point or reference for a locally adaptive threshold surface detection algorithm, which is able to capture the surface geometry at a sub-voxel resolution. In order to validate the methodology and confirm the envisaged benefits, selected geometry elements of the resulting surface geometry from measurements samples manufactured by additive manufacturing with integrated RIFD tags are compared to coordinate measurement machine (CMM) reference measurements. The results indicate a more robust surface geometry detection against artifacts especially for multi-material applications.