MeshSplat: Generalizable Sparse-View Surface Reconstruction via Gaussian
  Splatting
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
68ada9e6b9d0280e9ba2aaef
Hanzhi Chang
68ada9e6b9d0280e9ba2aaf0
Ruijie Zhu
claimed_verified
2025-08-26T19:05:38.566Z
6697ac8427e4e21a3a92da27
https://cdn-avatars.huggingface.co/v1/production/uploads/6697ac8427e4e21a3a92da27/9vn07-1_BBDk9zfDtDpcG.png
Ruijie Zhu
user
RuijieZhu
68ada9e6b9d0280e9ba2aaf1
Wenjie Chang
68ada9e6b9d0280e9ba2aaf2
Mulin Yu
68ada9e6b9d0280e9ba2aaf3
Yanzhe Liang
68ada9e6b9d0280e9ba2aaf4
Jiahao Lu
68ada9e6b9d0280e9ba2aaf5
Zhuoyuan Li
68ada9e6b9d0280e9ba2aaf6
Tianzhu Zhang
MeshSplat: Generalizable Sparse-View Surface Reconstruction via Gaussian
  Splatting
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
MeshSplat uses Gaussian Splatting and a feed-forward network to reconstruct surfaces from sparse views, improving accuracy with a Weighted Chamfer Distance Loss and normal prediction.
Gaussian Splatting 2DGS feed-forward network Weighted Chamfer Distance Loss normal prediction network monocular normal estimator sparse-view surface reconstruction