DE-3DGS: Depth-enhanced 3D Gaussian splatting via trinocular stereo matching and feature refinement

To address the challenges of stereo matching under sparse view conditions, which yield limited overlap, occlusion, and large parallax, a novel Depth Enhanced 3D Gaussian Splatting (DE-3DGS) framework is presented in this paper. By generating an intermediate virtual viewpoint image, disparity maps are obtained via trinocular stereo matching to improve depth reliability in wide baseline scenarios. The source images, together with the estimated depth map, are fused to predict the attributes and feature embeddings of the 3D Gaussian. In addition, a feature refinement module is proposed to improve the structural completeness and appearance coherence by refining incomplete regions. Experiments on THuman2.0 and THumanSit validate that the trinocular matching strategy of DE-3DGS, guided by intermediate viewpoints, significantly improves rendering quality and structural completeness in wide-baseline scenarios.