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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