---
license: bsd
pipeline_tag: depth-estimation
tags:
- Depth Completion
- ICCV2025
- Zero-shot
- Generalizable
- Lidar+RGB
library_name: pytorch
datasets:
- KITTI
- NYUv2
- ETH3D
- VOID
---

# 🌀 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](https://github.com/princeton-vl/OMNI-DC)  
**Huggingface Paper:** [https://huggingface.co/papers/2411.19278](https://huggingface.co/papers/2411.19278)

---

## 🧠 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 |

---

## 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}
}
```