Point clouds are utilized to enable automated engineering applications for their ability to represent spatial geometry. However, they inherently lack detailed surface textures, posing challenges in differentiating objects at the texture level. Hence, this study introduces a 2D–3D fusing approach, leveraging material properties recognized from registered images as an augmented feature to enhance deep learning methods for the segmentation of building elements within point clouds. The proposed method was evaluated quantitatively on a 3D indoor data set with an implementation in an office room. The results are promising, showing improvement in recognition performance, particularly for objects with similar geometry but having different material properties. For instance, the segmentation of boards increased by 70.87%, and doors improved by 41.06% using the PointNet architecture. This enhanced segmentation not only reduces the time for interpreting point clouds but also has the potential to benefit downstream applications such as Scan-to-building information modeling (BIM), as defining regions for objects is essential.

中文翻译：

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用于建筑元素的点云材料增强语义分割

点云用于实现自动化工程应用程序，因为它们能够表示空间几何形状。然而，它们本质上缺乏详细的表面纹理，这给在纹理级别区分物体带来了挑战。因此，本研究引入了 2D-3D 融合方法，利用从注册图像识别的材料属性作为增强特征来增强深度学习方法，以分割点云内的建筑元素。所提出的方法在 3D 室内数据集上进行了定量评估，并在办公室中实施。结果令人鼓舞，显示出识别性能的提高，特别是对于具有相似几何形状但具有不同材料特性的物体。例如，使用PointNet架构，板的分割提高了70.87%，门的分割提高了41.06%。这种增强的分割不仅减少了解释点云的时间，而且有可能使扫描到建筑信息模型 (BIM) 等下游应用受益，因为定义对象区域至关重要。