Complete sparse depth maps from many domains by training on simulated gaps with FillDepth

Complete sparse depth maps from many domains by training on simulated gaps with FillDepth

Towards Domain-agnostic Depth Completion

arXiv paper abstract https://arxiv.org/abs/2207.14466

arXiv PDF paper https://arxiv.org/pdf/2207.14466.pdf

Existing depth completion methods are often targeted at a specific sparse depth type, and generalize poorly across task domains.

... present a method to complete sparse/semi-dense, noisy, and potentially low-resolution depth maps obtained by various range sensors, including those in modern mobile phones, or by multi-view reconstruction algorithms.

... method leverages a data driven prior in the form of a single image depth prediction network trained on large-scale datasets, the output of which is used as an input to ... model.

... propose an effective training scheme where ... simulate various sparsity patterns in typical task domains.

... shows superior cross-domain generalization ability against state-of-the-art depth completion methods, introducing a practical solution to high quality depth capture on a mobile device ...

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