MeshSplat: Generalizable Sparse-View Surface Reconstruction via Gaussian
  Splatting
Authors: name: Hanzhi Chang, name: Ruijie Zhu, name: Wenjie Chang, name: Mulin Yu, name: Yanzhe Liang, name: Jiahao Lu, name: Zhuoyuan Li, name: Tianzhu Zhang
Organization: University of Science and Technology of China

Surface reconstruction has been widely studied in computer vision and
graphics. However, existing surface reconstruction works struggle to recover
accurate scene geometry when the input views are extremely sparse. To address
this issue, we propose MeshSplat, a generalizable sparse-view surface
reconstruction framework via Gaussian Splatting. Our key idea is to leverage
2DGS as a bridge, which connects novel view synthesis to learned geometric
priors and then transfers these priors to achieve surface reconstruction.
Specifically, we incorporate a feed-forward network to predict per-view
pixel-aligned 2DGS, which enables the network to synthesize novel view images
and thus eliminates the need for direct 3D ground-truth supervision. To improve
the accuracy of 2DGS position and orientation prediction, we propose a Weighted
Chamfer Distance Loss to regularize the depth maps, especially in overlapping
areas of input views, and also a normal prediction network to align the
orientation of 2DGS with normal vectors predicted by a monocular normal
estimator. Extensive experiments validate the effectiveness of our proposed
improvement, demonstrating that our method achieves state-of-the-art
performance in generalizable sparse-view mesh reconstruction tasks. Project
Page: https://hanzhichang.github.io/meshsplat_web