RoofDiffusion: Constructing Roofs from Severely Corrupted Point Data via Diffusion
<|reference_start|>RoofDiffusion: Constructing Roofs from Severely Corrupted Point Data via Diffusion: 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.<|reference_end|>
@article{lo2024roofdiffusion:,
    title={RoofDiffusion: Constructing Roofs from Severely Corrupted Point Data via
  Diffusion},
    author={Kyle Shih-Huang Lo, J"org Peters, Eric Spellman},
    journal={arXiv preprint arXiv:2404.09290},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.09290},
    primaryClass={cs.CV eess.IV}
}