High-detail semantic 3D building models are frequently utilized in robotics, geoinformatics, and computer vision. One key aspect
of creating such models is employing 2D conflict maps that detect openings’ locations in building facades. Yet, in reality, these
maps are often incomplete due to obstacles encountered during laser scanning. To address this challenge, we introduce FacaDiffy,
a novel method for inpainting unseen facade parts by completing conflict maps with a personalized Stable Diffusion model. Spe-
cifically, we first propose a deterministic ray analysis approach to derive 2D conflict maps from existing 3D building models and
corresponding laser scanning point clouds. Furthermore, we facilitate the inpainting of unseen facade objects into these 2D conflict
maps by leveraging the potential of personalizing a Stable Diffusion model. To complement the scarcity of real-world training
data, we also develop a scalable pipeline to produce synthetic conflict maps using random city model generators and annotated
facade images. Extensive experiments demonstrate that FacaDiffy achieves state-of-the-art performance in conflict map completion
compared to various inpainting baselines and increases the detection rate by 22% when applying the completed conflict maps for
high-definition 3D semantic building reconstruction. The code is be publicly available in the corresponding GitHub repository:
https://github.com/ThomasFroech/InpaintingofUnseenFacadeObjects
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High-detail semantic 3D building models are frequently utilized in robotics, geoinformatics, and computer vision. One key aspect
of creating such models is employing 2D conflict maps that detect openings’ locations in building facades. Yet, in reality, these
maps are often incomplete due to obstacles encountered during laser scanning. To address this challenge, we introduce FacaDiffy,
a novel method for inpainting unseen facade parts by completing conflict maps with a personalized Stable Diff...
»