ANormGS: normal prior guided anchored 3D Gaussian splatting for multiview scene reconstruction

To address the lack of geometric constraints in existing 3D Gaussian Splatting (3DGS) methods, we propose a novel geometry-aware optimization framework, ANormGS, which integrates anchor guidance with surface normal priors. To mitigate local distortions, Gaussian redundancy, and rendering artifacts caused by randomly oriented ellipsoids, ANormGS incorporates normal supervision into its anchor-based splatting pipeline. First, a dual optimization strategy is introduced, which combines normal-aware rendering with pretrained surface normals to align Gaussian orientations with underlying scene geometry. Second, we propose a scale-constrained flattening approach to enhance geometric compactness and suppress redundant representations. Extensive experiments on benchmark datasets, including Tanks and Temples, Deep Blending, and Mip-NeRF 360, demonstrate that ANormGS outperforms baseline methods, achieving an average PSNR gain of 0.14 dB, an SSIM improvement of 0.007, and an LPIPS reduction of 11.6%. These results highlight the effectiveness and robustness of our method across diverse and challenging 3D reconstruction scenarios.