D3GS: Depth, DINO, and RGB Diffusion Co-Guided 3D Gaussian Splatting for Sparse-View Reconstruction

2026-09-19

AI Summary

为解决3D高斯点云从稀疏视角重建时的几何模糊、视图不一致和细节缺失问题，提出D3GS框架，通过深度-迪诺-扩散指导方法联合增强几何与外观。

Abstract

Novel view synthesis from sparse inputs remains challenging for 3D Gaussian Splatting (3DGS) due to ambiguous geometry, cross-view inconsistency, and missing details in under-constrained regions, resulting in degraded reconstruction and unstable rendering. To tackle these issues, we propose D$^{3}$GS, a Depth-DINO-Diffusion guided sparse-view Gaussian reconstruction framework that jointly enhances geometry and appearance. D$^{3}$GS first recovers a high-resolution, metric depth map via diffusion-based completion and DPT (Dense Prediction Transformer) refinement, providing robust Gaussian initialization and geometric constraints. Then, a DINO-guided view-consistent learning is introduced to augment Gaussian attributes with structural features, improving multi-view consistency. Finally, a diffusion-based Gaussian refinement module injects generative priors into an iterative optimization strategy, enhancing high-frequency geometric and appearance details within the Gaussian representation. Experiments on DTU, LLFF, and Mip-NeRF 360 show that D$^{3}$GS achieves consistent and substantial improvements over strong baselines, with ablation studies validating the effectiveness and complementary roles of each component.

Problem

• Sparse-View Reconstruction
• 3D Gaussian Splatting
• Geometry Ambiguity
• Cross-View Inconsistency

Innovation

• Depth-DINO-Diffusion
• Gaussian Splatting
• Sparse-View Reconstruction
• Diffusion-based Refinement
• View-Consistent Learning