Targeted to the challenge of indoor scene understanding for intelligent devices, this paper question focuses on enhancing accuracy in semantic information extraction. A framework including a dual hierarchical attention network, transfer learning, interpretability analysis, and modeling module is applied to segment and reconstruct the indoor scene. A high-rise as-built building case is used to verify the method, the results show that: (1) the method achieves a high mIoU of 0.970 in point cloud segmentation and outperforms state-of-the-art methods, both demonstrating strong performance; (2) the method has sound feature extraction and learning ability in term of the interpretive analysis; (3) the method accelerates by 37 % than manual operations, achieving higher accuracy and efficiency. Overall, the method provides an effective solution to segment multi-class objects for indoor scene understanding and can serve as a basis for automated modeling to contribute to an accurate BIM model with great potential for practical application.

中文翻译：

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用于构建场景理解中点云语义分割的双分层注意力增强迁移学习


针对智能设备室内场景理解的挑战，本文问题侧重于提高语义信息提取的准确性。应用包括双分层注意力网络、迁移学习、可解释性分析和建模模块的框架来分割和重建室内场景。以高层竣工建筑为例对该方法进行了验证，结果表明：（1）该方法在点云分割中实现了0.970的高mIoU，优于现有方法，均表现出较强的性能;（2） 该方法在解释分析方面具有较好的特征提取和学习能力;（3） 该方法比手动操作加速 37%，实现了更高的准确性和效率。总体而言，该方法提供了一种有效的解决方案来分割多类对象以理解室内场景，并且可以作为自动建模的基础，有助于构建具有巨大实际应用潜力的准确 BIM 模型。