+ 
+ 
+ 
+ 
+
+ SolidGS: Consolidating Gaussian Surfel Splatting for
Sparse-View Surface Reconstruction
+
+
+ + 4Hong Kong University of Science and Technology + 5University of Hong Kong +
-
+
- + + + +
- + +
Abstract
+
+
+ We present SolidGS, which reconstructs a consolidated Gaussian field from sparse inputs. Given only three input views, our approach enables high-precision and detailed mesh extraction, and high-quality novel view synthesis, achieved within just three minutes.
+ ++ Gaussian splatting has achieved impressive improvements for both novel-view synthesis and surface reconstruction from multi-view images. However, current methods still struggle to reconstruct high-quality surfaces from only sparse view input images using Gaussian splatting. In this paper, we propose a novel method called SolidGS to address this problem. We observed that the reconstructed geometry can be severely inconsistent across multi-views, due to the property of Gaussian function in geometry rendering. This motivates us to consolidate all Gaussians by adopting a more solid kernel function, which effectively improves the surface reconstruction quality. With the additional help of geometrical regularization and monocular normal estimation, our method achieves superior performance on the sparse view surface reconstruction than all the Gaussian splatting methods and neural field methods on the widely used DTU, Tanks-and-Temples, and LLFF datasets. + +
++ + +
Main Idea
+
+ + We propose SolidGS, a novel representation that consolidates the opacity of Gaussians by introducing a shared, learnable solidness factor, enabling multi-view consistent geometry rendering. + We also introduce a new framework with geometric constraints to train our SolidGS representation, which consists of geometric priors and regularizations. +
+ ++ +
Reconstruction results on DTU
++ +
+ Our model's reconstruction results and comparisons on the DTU dataset. +
+
+ We show our mesh, rendered normal, and Novel View Synthesis (NVS) results on the DTU dataset. +
+Reconstruction results on Tanks-and-Temples (TNT)
++ +
+ Our model's reconstruction results and comparisons on the TNT dataset. Our model reconstructs the geometry with higher completeness and more details. +
+
+ We show our mesh, rendered depth, rendered normal, and Novel View Synthesis (NVS) results on the DTU dataset. +
++ +
+