Wavelet-guided and depth-aware optimization for high-fidelity 3D Gaussian splatting in computational scene rendering

High-fidelity rendering of complex scenes remains challenging for 3D Gaussian Splatting (3DGS), particularly under large viewpoint variations and in regions dominated by high-frequency structures, where aliasing and geometric inconsistencies frequently arise. To address these issues, we propose a wavelet-guided and depth-aware optimization framework that enhances frequency preservation and structural consistency in 3DGS-based computational imaging. A multi-scale wavelet decomposition is integrated to extract discriminative frequency components, which explicitly guide the reconstruction of high-frequency details during splat optimization. In parallel, a depth-aware weighting mechanism is incorporated into the rendering pipeline to adaptively modulate the contributions of spatially adjacent Gaussian primitives, improving geometric stability and mitigating artifacts caused by occlusion or rapid depth transitions. Furthermore, we introduce a high-frequency–weighted hybrid loss function based on L 1 distance and SSIM, enabling the network to more effectively capture fine structural variations while maintaining perceptual integrity. Experiments on complex indoor and outdoor scenes demonstrate that the proposed method achieves superior detail preservation, reduced aliasing, and improved rendering quality compared with existing 3DGS approaches. These results highlight the potential of frequency- and depth-aware optimization for advancing computational and AI-driven imaging techniques.