Confidence matters: Leveraging Multi-view Geometric Priors for GS-based Reconstruction

2026-08-06

AI Summary

This work addresses the degraded geometric quality often observed in 3D Gaussian splatting when reconstructing highly reflective objects, a limitation stemming from its reliance on Structure-from-Motion initialization and photometric optimization. To overcome this, the paper introduces, for the first time, a systematic integration of multi-view normal and depth maps as geometric priors, fused in a confidence-weighted manner to guide Gaussian parameter optimization. The proposed method leverages a Visual Geometry-Guided Transformer (VGGT) to estimate multi-view geometric quantities and employs confidence-aware adaptive fusion to enhance prior reliability. Experimental results demonstrate that this framework significantly outperforms existing approaches on standard benchmarks, achieving consistently robust geometric reconstruction—particularly in challenging scenes with high reflectance and complex geometry.

Abstract

3D Gaussian splatting (3DGS) has emerged as a widely-used tool for novel view synthesis, offering real-time rendering in a sparse representation. However, the method's reliance on structure-from-motion initialization and photometric optimization can lead to suboptimal geometric reconstruction, particularly for objects with high specularity. In this work, we investigate the integration of geometric priors, in the form of predicted normal and depth maps, into the 3DGS framework to improve the reconstruction quality. We analyze the effect of incorporating these priors into GS-based methods and our evaluation reveals that multi-view predictions, as they are done by the recent visual geometry grounded transformer (VGGT), outperform single-view alternatives. A major factor is the existence of a confidence map for the estimations, which comes as a by-product of multi-view models and which can significantly improve the effectiveness of priors by weighting each prediction appropriately. Extensive experiments on standard benchmarks show consistent improvement in reconstruction quality and significant gains in complex scenes including specular objects.

Problem

• 3D Gaussian splatting
• geometric reconstruction
• specular objects
• multi-view priors
• confidence estimation

Innovation

• 3D Gaussian Splatting
• geometric priors
• multi-view consistency
• confidence map
• specular objects