RoofDiffusion: Constructing Roofs from Severely Corrupted Point Data via
  Diffusion
Kyle Shih-Huang Lo Jörg Peters Eric Spellman
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 heights. Both
quantitatively outperform state-of-the-art unguided depth completion and
representative inpainting methods for Digital Elevation Models (DEM), on both a
roof-specific benchmark and the BuildingNet dataset. Qualitative assessments
show the effectiveness of RoofDiffusion for datasets with real-world scans
including AHN3, Dales3D, and USGS 3DEP LiDAR. Tested with the leading City3D
algorithm, preprocessing height maps with RoofDiffusion noticeably improves 3D
building reconstruction. RoofDiffusion is complemented by a new dataset of 13k
complex roof geometries, focusing on long-tail issues in remote sensing; a
novel simulation of tree occlusion; and a wide variety of large-area roof
cut-outs for data augmentation and benchmarking.