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Guided pluralistic building contour completion


Image/sketch completion is a core task that addresses the problem of completing the missing regions of an image/sketch with realistic and semantically consistent content. We address one type of completion which is producing a tentative completion of an aerial view of the remnants of a building structure. The inference process may start with as little as 10% of the structure and thus is fundamentally pluralistic (e.g., multiple completions are possible). We present a novel pluralistic building contour completion framework. A feature suggestion component uses an entropy-based model to request information from the user for the next most informative location in the image. Then, an image completion component trained using self-supervision and procedurally generated content produces a partial or full completion. In our synthetic and real-world experiments for archaeological sites in Turkey, with up to only 4 iterations, we complete building footprints having only 10–15% of the ancient structure initially visible. We also compare to various state-of-the-art methods and show our superior quantitative/qualitative performance. While we show results for archaeology, we anticipate our method can be used for restoring highly incomplete historical sketches and for modern day urban reconstruction despite occlusions.

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This research was funded in part by National Science Foundation grants #1816514 CHS: Small: Functional Proceduralization of 3D Geometric Models, #1835739 U-Cube: A Cyberinfrastructure for Unified and Ubiquitous Urban Canopy Parameterization, and #2107096 Deep Generative Modeling for Urban and Archaeological Recovery

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Correspondence to Xiaowei Zhang.

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Zhang, X., Ma, W., Varinlioglu, G. et al. Guided pluralistic building contour completion. Vis Comput 38, 3205–3216 (2022).

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