FoundDSR: A Generalizable Foundation Model with Guided 2D Gaussian Splatting for Depth Super-Resolution

arXiv:2609.32323 · cs.CV · Submitted 2026-09-26 · Read on arXiv

cs.CV

Submitted: 2026-09-26

Updated: 2026-09-26

Code: https://github.com/yanzq95/FoundDSR

Terminology

• Foundation Model — 28× in this paper · explained in 3 episodes
• Generalization — 22× in this paper · explained in 13 episodes
• Federated Learning — 17× in this paper · explained in 14 episodes
• Gaussian Splatting — 17× in this paper · explained in 4 episodes
• Foundation Models — 15× in this paper · explained in 7 episodes
• Zero-Shot Generalization — 11× in this paper · explained in 5 episodes
• Ablation — 9× in this paper · explained in 1 episode
• Aggregation — 9× in this paper · explained in 1 episode
• Ablation study — 8× in this paper · explained in 1 episode
• Benchmark — 8× in this paper · explained in 2 episodes
• Robustness — 7× in this paper · explained in 5 episodes
• Consistency — 5× in this paper · explained in 3 episodes
• Accuracy — 4× in this paper · explained in 1 episode
• Local Optimization — 4× in this paper · explained in 2 episodes
• Loss Function — 4× in this paper · explained in 2 episodes
• Decoupling — 2× in this paper · explained in 2 episodes
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• Generative AI — 2× in this paper · explained in 2 episodes
• Calibration — 1× in this paper · explained in 7 episodes
• Computational overhead — 1× in this paper · explained in 2 episodes
• Contrastive Learning — 1× in this paper · explained in 5 episodes
• Core — 1× in this paper · explained in 2 episodes
• Data Augmentation — 1× in this paper · explained in 7 episodes
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Elias — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Federated Learning (FL) — 1× in this paper · explained in 7 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Gaussian Splatting (GS) — 1× in this paper · explained in 3 episodes
• Iterative Refinement — 1× in this paper · explained in 3 episodes
• Latent Diffusion Model — 1× in this paper · explained in 2 episodes
• Matrix Completion — 1× in this paper · explained in 3 episodes
• Multi-modal Inputs — 1× in this paper · explained in 2 episodes
• Reconstruction Error — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Transformer — 1× in this paper · explained in 2 episodes
• Verification — 1× in this paper · explained in 2 episodes

Sources

• Decoupled Weight Decay Regularization
• DINOv2: Learning Robust Visual Features without Supervision
• The Replica Dataset: A Digital Replica of Indoor Spaces
• Masked Depth Modeling for Spatial Perception
• InfiniDepth: Arbitrary-Resolution and Fine-Grained Depth Estimation with Neural Implicit Fields
• Large Depth Completion Model from Sparse Observations

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