HR-2DGS: Hybrid regularization for sparse-view 3D reconstruction with 2D Gaussian splatting

Sparse-view 3D reconstruction has garnered widespread attention due to its demand for high-quality reconstruction under low-sampling data conditions. Existing NeRF-based methods rely on dense views and substantial computational resources, while 3DGS is limited by multi-view inconsistency and insufficient geometric detail recovery, making it challenging to achieve ideal results in sparse-view scenarios. This paper introduces HR-2DGS, a novel hybrid regularization framework based on 2D Gaussian Splatting (2DGS), which significantly enhances multi-view consistency and geometric recovery by dynamically fusing monocular depth estimates with rendered depth maps, incorporating hybrid normal regularization techniques. To further refine local details, we introduce a per-pixel depth normalization that leverages each pixel’s neighborhood statistics to emphasize fine-scale geometric variations. Experimental results on the LLFF and DTU datasets demonstrate that HR-2DGS outperforms existing methods in terms of PSNR, SSIM, and LPIPS, while requiring only 2.5GB of memory and a few minutes of training time for efficient training and real-time rendering.