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arXiv · 2610.29177

ROCOCO: Robust Outline Completion of Occluded Curvilinear Objects

Ken Zheng·Jaimyn Drake
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Abstract

We propose a learning-based generative model for completing curvilinear features under occlusion by environmental obstacles. To this end, we adopt a flow matching-based approach that supports the inpainting of occluded regions with the appropriate curvilinear features. To eliminate the reliance on privileged information about the occlusion a priori, we also train a UNet-based mask prediction model, which can identify regions for inpainting given only the occluded observation. The components of our approach are validated via experiments using procedurally-generated test cases, and the complete pipeline is validated using large public datasets for curvilinear objects. Against off-the-shelf inpainting baselines, ROCOCO is the strongest method by a wide margin when no occlusion mask is given, and is competitive with mask-supervised inpainting when one is, achieving best on full-image error and second only to LaMa on the occluded region, while using a completer of $7.7$M parameters against LaMa's $51$M and Stable Diffusion's $860$M.

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Ken Zheng, Jaimyn Drake. 2026-10-21. ROCOCO: Robust Outline Completion of Occluded Curvilinear Objects. https://arxiv.org/abs/2610.29177

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