AI 中文总结
FoundDSR利用引导式2D高斯溅射和异构联邦学习，构建可泛化的深度超分辨率基础模型，在多种零样本条件下优于现有方法。
AI 中文摘要
我们提出了FoundDSR，一种可泛化的基础模型，用于在未知数据分布下利用RGB-D对进行稳健的深度重建。FoundDSR首先采用引导式2D高斯溅射策略，以高斯原语建模深度表示。该策略利用高分辨率RGB作为提示来优化高斯参数，从而促使每个高斯原语沿高频结构方向各向异性变形。由此产生的高斯上采样表示随后通过有效的深度重建分支映射到高分辨率深度。此外，为了缓解大规模异构数据中分布差异导致的训练不稳定性和对主导来源的偏差，我们引入了异构联邦学习，将每个数据源分配给独立的客户端进行局部优化和全局聚合。这种设计有效地赋予FoundDSR对多样化和大规模训练数据的稳定可扩展性。在合成、真实世界、任意尺度和噪声条件下的广泛零样本评估表明，FoundDSR始终优于现有最先进方法，证实了其对未知场景的强大鲁棒性和泛化能力。
英文摘要
We introduce FoundDSR, a generalizable foundation model for robust depth reconstruction across unseen data distributions using RGB-D pairs. FoundDSR begins with a guided 2D Gaussian Splatting strategy to model depth representations with Gaussian primitives. This strategy employs high-resolution RGB as prompts to optimize the Gaussian parameters, thereby encouraging each Gaussian primitive to anisotropically deform along high-frequency structural directions. The resulting Gaussian-upsampled representations are then mapped to high-resolution depth through an effective depth reconstruction branch. Furthermore, to mitigate training instability and bias toward dominant sources caused by distribution gaps in large-scale heterogeneous data, we introduce heterogeneous federated learning that allocates each data source to an independent client for local optimization and global aggregation. This design effectively endows FoundDSR with stable scalability to diverse and large-scale training data. Extensive zero-shot evaluations on synthetic, real-world, arbitrary-scale, and noisy conditions demonstrate that FoundDSR consistently outperforms existing state-of-the-art approaches, confirming its strong robustness and generalization to unknown scenes.
