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arXiv · 2411.19278

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

Yiming Zuo·Willow Yang·Zeyu Ma·Jia Deng
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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.

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Yiming Zuo, Willow Yang, Zeyu Ma, Jia Deng. 2024-11-28. OMNI-DC: Highly Robust Depth Completion with Multiresolution Depth Integration. https://arxiv.org/abs/2411.19278

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