VolSplat: Rethinking Feed-Forward 3D Gaussian Splatting with
  Voxel-Aligned Prediction
Feed-forward 3D Gaussian Splatting (3DGS) has emerged as a highly effective
solution for novel view synthesis. Existing methods predominantly rely on a
pixel-aligned Gaussian prediction paradigm, where each 2D pixel is mapped to a
3D Gaussian. We rethink this widely adopted formulation and identify several
inherent limitations: it renders the reconstructed 3D models heavily dependent
on the number of input views, leads to view-biased density distributions, and
introduces alignment errors, particularly when source views contain occlusions
or low texture. To address these challenges, we introduce VolSplat, a new
multi-view feed-forward paradigm that replaces pixel alignment with
voxel-aligned Gaussians. By directly predicting Gaussians from a predicted 3D
voxel grid, it overcomes pixel alignment's reliance on error-prone 2D feature
matching, ensuring robust multi-view consistency. Furthermore, it enables
adaptive control over Gaussian density based on 3D scene complexity, yielding
more faithful Gaussian point clouds, improved geometric consistency, and
enhanced novel-view rendering quality. Experiments on widely used benchmarks
including RealEstate10K and ScanNet demonstrate that VolSplat achieves
state-of-the-art performance while producing more plausible and view-consistent
Gaussian reconstructions. In addition to superior results, our approach
establishes a more scalable framework for feed-forward 3D reconstruction with
denser and more robust representations, paving the way for further research in
wider communities. The video results, code and trained models are available on
our project page: https://lhmd.top/volsplat.
68d360650e215259d193b245
Weijie Wang
claimed_verified
2025-09-24T13:53:11.784Z
66699aa8a33847217b5a49c7
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/u8Z-6U8U7ARXOpdBDI7Qm.png
Weijie Wang
user
lhmd
68d360650e215259d193b246
Yeqing Chen
68d360650e215259d193b247
Zeyu Zhang
claimed_verified
2025-09-24T13:53:09.030Z
64ec877bb93654d4ca5c92e9
https://cdn-avatars.huggingface.co/v1/production/uploads/64ec877bb93654d4ca5c92e9/GvHk_KSdE9Rhnk_o-NaZX.jpeg
Zeyu Zhang
user
SteveZeyuZhang
68d360650e215259d193b248
Hengyu Liu
68d360650e215259d193b249
Haoxiao Wang
68d360650e215259d193b24a
Zhiyuan Feng
68d360650e215259d193b24b
Wenkang Qin
68d360650e215259d193b24c
Zheng Zhu
68d360650e215259d193b24d
Donny Y. Chen
68d360650e215259d193b24e
Bohan Zhuang
VolSplat: Rethinking Feed-Forward 3D Gaussian Splatting with
  Voxel-Aligned Prediction
Feed-forward 3D Gaussian Splatting (3DGS) has emerged as a highly effective
solution for novel view synthesis. Existing methods predominantly rely on a
pixel-aligned Gaussian prediction paradigm, where each 2D pixel is mapped to a
3D Gaussian. We rethink this widely adopted formulation and identify several
inherent limitations: it renders the reconstructed 3D models heavily dependent
on the number of input views, leads to view-biased density distributions, and
introduces alignment errors, particularly when source views contain occlusions
or low texture. To address these challenges, we introduce VolSplat, a new
multi-view feed-forward paradigm that replaces pixel alignment with
voxel-aligned Gaussians. By directly predicting Gaussians from a predicted 3D
voxel grid, it overcomes pixel alignment's reliance on error-prone 2D feature
matching, ensuring robust multi-view consistency. Furthermore, it enables
adaptive control over Gaussian density based on 3D scene complexity, yielding
more faithful Gaussian point clouds, improved geometric consistency, and
enhanced novel-view rendering quality. Experiments on widely used benchmarks
including RealEstate10K and ScanNet demonstrate that VolSplat achieves
state-of-the-art performance while producing more plausible and view-consistent
Gaussian reconstructions. In addition to superior results, our approach
establishes a more scalable framework for feed-forward 3D reconstruction with
denser and more robust representations, paving the way for further research in
wider communities. The video results, code and trained models are available on
our project page: https://lhmd.top/volsplat.
VolSplat, a voxel-aligned Gaussian prediction method, improves novel view synthesis by overcoming pixel alignment limitations and enhancing 3D reconstruction quality.
feed-forward 3D Gaussian Splatting pixel-aligned Gaussian prediction voxel-aligned Gaussians 3D voxel grid Gaussian point clouds geometric consistency novel-view rendering quality RealEstate10K ScanNet