Coded targets are widely used in close-range photogrammetry and 3D laser scanning for automated referencing and registration. However, most fiducial systems are optimized for specific software environments, limiting interoperability across processing pipelines. This study presents a cross-platform coded target framework for multi-sensor 3D acquisition that combines geometric redundancy, binary encoding, and human-readable elements to enhance robustness and reproducibility. An open-source implementation (PGT-Toolkit) supports marker generation, detection, and standardized coordinate export. Performance was evaluated using a controlled laboratory framework with systematically varied viewing angles, distances, and illumination conditions. Experiments were conducted using DSLR-based photogrammetry and terrestrial laser scanning. Detection rate, centroid repeatability, reprojection error, and cross-platform coordinate consistency were assessed and compared with those of established fiducial systems. Results demonstrate stable detection under oblique viewing geometries and consistent coordinate estimation across both commercial and open-source software environments. Laboratory studies confirm that Human Readable Coded Targets (HRCT) provide reliable, accurate, and cross-platform compatibility for both photogrammetric and 3D laser scanning workflows, which remain to be verified by field studies. The proposed framework contributes a structured methodology for experimental validation of interoperable coded targets in multi-sensor 3D workflows.