View-surface-aware PBR Gaussian splatting for sparse-view 3D reconstruction

Abstract

Efficient 3D reconstruction and high-fidelity novel-view synthesis under sparse-view, real-time constraints remain challenging. Implicit methods like Neural Radiance Fields (NeRF) depend on dense data and long optimization, while explicit 3D Gaussian Splatting (3D-GS) often loses geometric-photometric consistency and editable materials under sparse inputs. We propose a 3D-GS-based framework that attaches a simplified Disney Bidirectional Reflectance Distribution Function (BRDF) and differentiable physically based rendering (PBR) shader to each surface Gaussian for interpretable material attributes. A view-surface-aware selective densification strategy enhances sparse-region alignment, and a two stage training pipeline balances global consistency and surface fidelity, with 64 incident rays per Gaussian for accurate specular reflection. On MVImgNet and DTU dataset, our method achieves consistent improvements under 6-view settings: +0.4dB Peak Signal-to-Noise Ratio (PSNR), +0.5% Structural Similarity Index Measure (SSIM), and -5.6% Learned Perceptual Image Patch Similarity (LPIPS) on MVImgNet, and +0.6dB PSNR and +2.9% SSIM on DTU, compared with InstantSplat-XL. The proposed densification further improves PSNR by 0.7dB with only 20% higher training time. Overall, our framework delivers higher geometric accuracy, improved material realism, and real-time rendering performance suitable for applications such as Simultaneous Localization and Mapping (SLAM) and relighting.