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 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.
Abstract (translated)
准确地完成和去噪屋顶高度图对于重建高质量的3D建筑至关重要。修复稀疏点可以提高低成本传感器使用并减少无人机飞行重叠。RoofDiffusion是一种新的端到端自监督扩散技术，特别适用于完成艰难、高度不连续的屋顶高度图。RoofDiffusion利用广泛可用的心跳图，可以处理多达99%的点稀疏和80%的屋顶面积遮挡（区域不完整性）。一种变体，No-FP RoofDiffusion同时预测建筑轮廓和高度。在屋顶特定基准和BuildingNet数据集上，No-FP RoofDiffusion的定量效果超过了目前最先进的未经指导的深度完成和代表性的修复方法。定性评估显示，RoofDiffusion对于包括AHN3、Dales3D和USGS 3DEP LiDAR等现实世界扫描的数据集非常有效。使用领先的City3D算法进行测试，使用RoofDiffusion预处理屋顶图显著提高了3D建筑重建。RoofDiffusion通过一个新的具有13k个复杂屋顶几何的 datasets，重点关注遥感中的长尾问题；一种新的树遮挡模拟；以及各种大面积屋顶切口，用于数据增强和基准测试而得到了补充。
URL
https://arxiv.org/abs/2404.09290
