Guided pluralistic building contour completion
Guided pluralistic building contour completion
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DOI：
10.1007/s00371-022-02532-z
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发表时间：
2022-06-08
期刊：
影响因子：
3.5
通讯作者：
Aliaga，Daniel
中科院分区：
文献类型：
--
作者：
Zhang，Xiaowei;Ma，Wufei;Aliaga，Daniel
图像/草图完成是一项核心任务，它解决了用逼真和语义一致的内容完成图像/草图的缺失区域的问题。我们解决一种类型的完成，这是生产一个初步完成的鸟瞰图的残余建筑结构。推理过程可以从结构的10%开始，因此基本上是多元的（例如，多个完成是可能的）。我们提出了一种新颖的多元建筑轮廓完成框架。特征建议组件使用基于熵的模型来向用户请求图像中下一个信息量最大的位置的信息。然后，使用自我监督和程序生成的内容训练的图像完成组件产生部分或全部完成。在我们对土耳其考古遗址的合成和真实世界的实验中，只有4次迭代，我们完成的建筑足迹只有最初可见的古代结构的10-15%。我们还比较了各种国家的最先进的方法，并显示我们的上级定量/定性性能。虽然我们展示了考古学的结果，但我们预计我们的方法可以用于恢复高度不完整的历史草图和现代城市重建，尽管有闭塞。
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.