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RoofDiffusion: Constructing Roofs from Severely Corrupted Point Data via Diffusion
April 16, 2024, 4:47 a.m. | Kyle Shih-Huang Lo, J\"org Peters, Eric Spellman
cs.CV updates on arXiv.org arxiv.org
Abstract: Accurate completion and denoising of roof height maps are crucial to reconstructing high-quality 3D buildings. Repairing sparse points can enhance low-cost sensor use and reduce UAV flight overlap. RoofDiffusion is a new end-to-end self-supervised diffusion technique for robustly completing, in particular difficult, roof height maps. RoofDiffusion leverages widely-available curated footprints and can so handle up to 99\% point sparsity and 80\% roof area occlusion (regional incompleteness). A variant, No-FP RoofDiffusion, simultaneously predicts building footprints and …
abstract arxiv buildings cost cs.cv data denoising diffusion eess.iv low maps quality reduce sensor type via
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