🌀 OMNI-DC: Highly Robust Depth Completion with Multiresolution Depth Integration

Authors: Yiming Zuo, Willow Yang, Zeyu Ma, Jia Deng
Institution: Princeton Vision & Learning Lab
Paper: OMNI-DC: Highly Robust Depth Completion with Multiresolution Depth Integration, ICCV 2025
Code: GitHub – princeton-vl/OMNI-DC
Huggingface Paper: https://huggingface.co/papers/2411.19278

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🧠 Model Overview

OMNI-DC is a generalizable depth completion framework that integrates sparse depth signals at multiple spatial resolutions to produce dense, metric-accurate depth maps.
It is trained on a mixture of synthetic and real data, achieving strong cross-dataset generalization and robustness to unseen sparsity patterns.

Key highlights:

• Multiresolution sparse-depth integration
• Differentiable optimization layer for enforcing geometric consistency
• Trained on diverse synthetic datasets with real-world fine-tuning
• Compatible with Depth Anything v2 as a plug-in prior (v1.1+)

🧩 Use Cases

Use case	Description
Zero-shot depth completion	Apply to new datasets without retraining
SLAM / mapping	Fill dense maps from sparse LiDAR or RGB-D sensors
Novel view synthesis	Supply high-quality depth for 3D scene reconstruction

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If you use OMNI-DC, please cite:

@inproceedings{zuo2025omni,
  title     = {OMNI-DC: Highly Robust Depth Completion with Multiresolution Depth Integration},
  author    = {Zuo, Yiming and Yang, Willow and Ma, Zeyu and Deng, Jia},
  booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
  year      = {2025}
}