Among the image-based methods, photogrammetry is a consolidated 3D reconstruction technique able to provide highly accurate metric products, widely exploited in many domains. Photogrammetry is, however, conditioned by the characteristics of the captured scene, with good performance in well-textured areas and limits when non-collaborative surfaces, such as reflective or transparent, are present. In such cases, the photogrammetric reconstruction is often affected by noise, incomplete geometry and artifacts, reducing its final reconstruction quality. In recent years, different AI-based reconstruction methods have emerged as alternative (or complementary) 3D reconstruction and rendering solutions. In particular, 3D Gaussian Splatting (GS) has demonstrated impressive capabilities in rendering photorealistic scenes in challenging situations with high visual fidelity. However, its application in large-scale scenarios or when highly accurate 3D metric products are required is still limited, due to the high computational resources needed and the intrinsic optimization of GS methods for photometric rendering quality. To address these bottlenecks, this work proposes a hybrid reconstruction pipeline, leveraging the strengths and benefits of each technique. The method exploits the accurate geometry of photogrammetry in well-textured regions and the GS capabilities to improve completeness and visual aspect in areas featuring non-collaborative surfaces. A fusion strategy is proposed to combine the two results into a single 3D model, presenting examples from two aerial and one terrestrial dataset.