The occlusion issues of computer vision (CV) applications in construction have attracted significant attention, especially those caused by the wide-coverage, crisscrossed, and immovable scaffold. Intuitively, removing the scaffold and restoring the occluded visual information can provide CV agents with clearer site views and thus help them better understand the construction scenes. Therefore, this study proposes a novel two-step method combining pixel-level segmentation and image inpainting for restoring construction scenes from scaffold occlusion. A low-cost data synthesis method based only on unlabeled data is developed to address the shortage dilemma of labeled data. Experiments on the synthesized test data show that the proposed method achieves performances of 92% mean intersection over union (MIoU) for scaffold segmentation and over 82% structural similarity (SSIM) for scene restoration from scaffold occlusion.

翻译：计算机视觉（CV）在建筑领域的遮挡问题已引起广泛关注，特别是由大面积覆盖、纵横交错且不可移动的脚手架造成的遮挡。直观而言，移除脚手架并恢复被遮挡的视觉信息能为视觉智能体提供更清晰的施工现场视野，从而帮助其更好地理解建筑场景。为此，本研究提出一种新颖的两步法，结合像素级分割与图像修复技术，从脚手架遮挡中恢复建筑场景。针对标注数据短缺问题，开发了一种仅基于无标注数据的低成本数据合成方法。在合成测试数据上的实验表明：所提方法的脚手架分割平均交并比（MIoU）达到92%，遮挡场景恢复的结构相似性（SSIM）超过82%。