OMNI-DC: Highly Robust Depth Completion with Multiresolution Depth Integration
Yiming Zuo, Willow Yang, Zeyu Ma, Jia Deng
Abstract
Depth completion (DC) aims to predict a dense depth map from an RGB image and a sparse depth map. Existing DC methods generalize poorly to new datasets or unseen sparse depth patterns, limiting their real-world applications. We propose OMNI-DC, a highly robust DC model that generalizes well zero-shot to various datasets. The key design is a novel Multi-Resolution Depth Integrator, allowing our model to deal with very sparse depth inputs. We also introduce a novel Laplacian loss to model the ambiguity in the training process. Moreover, we train OMNI-DC on a mixture of high-quality datasets with a scale normalization technique and synthetic depth patterns. Extensive experiments on 7 datasets show consistent improvements over baselines, reducing errors by as much as 43%. Codes and checkpoints are available at https://github.com/princeton-vl/OMNI-DC.
BibTeX
@InProceedings{Zuo_2025_ICCV,
author = {Zuo, Yiming and Yang, Willow and Ma, Zeyu and Deng, Jia},
title = {OMNI-DC: Highly Robust Depth Completion with Multiresolution Depth Integration},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {9287-9297}
}