Indoor environment model reconstruction has emerged as a significant and challenging task in terms of the provision of a semantically rich and geometrically accurate indoor model. Recently, there has been an increasing amount of research related to indoor environment reconstruction. Therefore, this paper reviews the state-of-the-art techniques for the three-dimensional (3D) reconstruction of indoor environments. First, some of the available benchmark datasets for 3D reconstruction of indoor environments are described and discussed. Then, data collection of 3D indoor spaces is briefly summarized. Furthermore, an overview of the geometric, semantic, and topological reconstruction of the indoor environment is presented, where the existing methodologies, advantages, and disadvantages of these three reconstruction types are analyzed and summarized. Finally, future research directions, including technique challenges and trends, are discussed for the purpose of promoting future research interest. It can be concluded that most of the existing indoor environment reconstruction methods are based on the strong Manhattan assumption, which may not be true in a real indoor environment, hence limiting the effectiveness and robustness of existing indoor environment reconstruction methods. Moreover, based on the hierarchical pyramid structures and the learnable parameters of deep-learning architectures, multi-task collaborative schemes to share parameters and to jointly optimize each other using redundant and complementary information from different perspectives show their potential for the 3D reconstruction of indoor environments. Furthermore, indoor–outdoor space seamless integration to achieve a full representation of both interior and exterior buildings is also heavily in demand.

中文翻译：

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室内环境的3D重建技术综述

就提供语义丰富且几何精确的室内模型而言，室内环境模型重建已成为一项重大而具有挑战性的任务。近来，与室内环境重建有关的研究越来越多。因此，本文回顾了用于室内环境的三维（3D）重建的最新技术。首先，描述和讨论了一些可用于室内环境3D重建的基准数据集。然后，简要总结了3D室内空间的数据收集。此外，还介绍了室内环境的几何，语义和拓扑结构重建的概述，并分析和总结了这三种重建类型的现有方法，优点和缺点。最后，讨论未来的研究方向，包括技术挑战和趋势，以提高未来的研究兴趣。可以得出的结论是，大多数现有的室内环境重建方法都是​​基于强曼哈顿假设，在真实的室内环境中可能并非如此，因此限制了现有室内环境重建方法的有效性和鲁棒性。此外，基于分层金字塔结构和深度学习架构的可学习参数，使用多任务协作方案共享参数并使用冗余和互补信息相互优化的多角度协作方案从不同角度展示了其在室内环境3D重建中的潜力。此外，