| Popis: |
Abstract Dougong, a distinctive component of ancient Chinese wooden architecture, holds significant importance for the preservation and restoration of such structures. In particular, the northern official-style buildings represent the pinnacle of ancient Chinese construction techniques. In the realm of cultural heritage preservation, the application of deep learning has gradually expanded, demonstrating remarkable effectiveness. Point cloud serving as a crucial source for Dougong, encapsulates various information, enabling support for tasks like Dougong point cloud classification and completion. The quality of Dougong datasets directly impacts the outcomes of DNNs (deep neural networks), as they serve as the foundational data support for these models. The typical official-style Dougong, with its standardized and repetitive structural patterns, is highly suitable for training DNNs to accurately recognize and analyze these complex architectural elements. However, due to the inherent characteristics of Dougong, such as coplanarity and occlusion, acquiring point cloud data is challenging, resulting in poor data quality and organizational difficulties. To address this issue, our study adopts a multi-source data fusion approach to tackle the challenges of insufficient data quantity and poor data quality. Further, through data augmentation, we enhance the dataset’s volume and generalize its characteristics. This effort culminates in the creation of the typical official-style Dougong Point Cloud Dataset (DG Dataset), poised to support deep learning tasks related to Dougong scenarios. |
