A Hybrid Approach using Gaussian Splatting and Parametric Models based on 3D Renders for Real-Time Visualisation

Abstract

Abstract. This study investigates the use of synthetic images generated within Blender for reconstruction via 3D Gaussian Splatting (3DGS). These synthetic images are derived from a 4D parametric model of a Rhenish castle, incorporating its surroundings and distant environment. While such parametric models offer high-fidelity data, they are computationally intensive for real-time applications. 3DGS is therefore employed to produce high-quality visualisations from images with known spatial orientations. Two reconstruction methods are compared in this study: the open-source native code and the commercial Postshot solution with its Splat3 model. The primary objective is to demonstrate the applicability of this method using synthetic imagery to create lightweight visualisations of digital twins of theoretical 4D states. The underlying parametric model, comprising numerous distinct objects and procedural textures, achieves high photorealism at the expense of substantial computational resources. Consequently, the reconstruction of this dataset via 3DGS facilitates the export and online dissemination of the complex model, decoupling visualisation quality from geometric complexity. The approach is quantitatively validated by comparing the 3DGS output against the original ground truth. Results demonstrate that the Splat3 model outperforms the native open-source approach in visual fidelity, processing speed, and geometric accuracy when handling high-resolution datasets. Both reconstruction methods achieve rendering performances well above 100 frames per second. This confirms that 3DGS can successfully be used with synthetic images to transform computationally heavy parametric models into highly optimised digital representations, ensuring near real-time visualisation suitable for immersive virtual reality and public dissemination.