AdaptSplat: Adapting Vision Foundation Models for Feed-Forward 3D Gaussian Splatting
This work explores a simple yet powerful lightweight adapter design for feed-forward 3D Gaussian Splatting (3DGS). Existing methods typically apply complex, architecture-specific designs on top of the generic pipeline of image feature extraction $\rightarrow$ multi-view interaction $\rightarrow$ feature decoding. However, constrained by the scale bottleneck of 3D training data and the low-pass filtering effect of deep networks, these methods still fall short in cross-domain generalization and high-frequency geometric fidelity. To address these problems, we propose AdaptSplat, which demonstrates that without complex component engineering, introducing a single adapter of only 1.5M parameter...
| Authors | Mingwei Xing, Xinliang Wang, Yifeng Shi |
|---|---|
| Primary category | cs.CV |
| Published | 11 May 2026 |
| Last updated at source | 6 September 2026 |
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