ROCOCO: Robust Outline Completion of Occluded Curvilinear Objects

Key points

• To this end, we adopt a flow matching-based approach that supports the inpainting of occluded regions with the appropriate curvilinear features.1
• 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.1
• 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.1
• 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.1

Sources (1)

• [1]ROCOCO: Robust Outline Completion of Occluded Curvilinear Objects
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 21, 04:09 AM

  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.

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