We present a hybrid approach for modeling complex interior structural elements from the unstructured point cloud without additional information. The proposed approach focuses on an integrated modeling strategy that can reconstruct structural elements and keep the balance of model completeness and quality. First, a data-driven approach detects the complete structure points of indoor scenarios including the curved wall structures and detailed structures. After applying the down-sampling process to point cloud dataset, ceiling and floor points are detected by RANSAC. The ceiling boundary points are selected as seed points of the growing algorithm to acquire points related to the wall segments. Detailed structures points are detected using the Grid-Slices analysis approach. Second, a model-driven refinement is conducted to the structure points that aims to decrease the impact of point cloud accuracy on the quality of the model. RANSAC algorithm is implemented to detect more accurate layout, and the hole in structure points is repaired in this refinement step. Lastly, the Screened Poisson surface reconstruction approach is conducted to generate the model based on the structure points after refinement. Our approach was validated on the backpack laser dataset, handheld laser dataset, and synthetic dataset, and experimental results demonstrate that our approach can preserve the curved wall structures and detailed structures in the model with high accuracy.

中文翻译：

---

结构元素检测与重建（SEDR）：复杂的室内结构建模的混合方法

我们提出了一种混合方法，用于从非结构化点云建模复杂的内部结构元素，而无需其他信息。所提出的方法侧重于一种集成的建模策略，该策略可以重构结构元素并保持模型完整性和质量的平衡。首先，数据驱动的方法可以检测室内场景的完整结构点，包括弯曲的墙壁结构和详细的结构。在将下采样过程应用于点云数据集之后，RANSAC将检测到天花板和地板点。选择天花板边界点作为生长算法的种子点，以获取与墙段有关的点。使用网格切片分析方法可以检测详细的结构点。第二，对结构点进行模型驱动的细化，目的是减少点云精度对模型质量的影响。实施RANSAC算法以检测更准确的布局，并在此细化步骤中修复结构点中的孔。最后，采用筛选泊松表面重构方法，根据细化后的结构点生成模型。我们的方法在背包激光数据集，手持式激光数据集和合成数据集上得到了验证，实验结果表明，该方法可以在模型中高精度地保留弯曲的壁结构和详细结构。并在此细化步骤中修复结构点中的孔。最后，采用筛选泊松表面重构方法，根据细化后的结构点生成模型。我们的方法在背包激光数据集，手持式激光数据集和合成数据集上得到了验证，实验结果表明我们的方法可以在模型中高精度地保留弯曲的壁结构和详细结构。并在此细化步骤中修复结构点中的孔。最后，采用筛选泊松表面重构方法，根据细化后的结构点生成模型。我们的方法在背包激光数据集，手持式激光数据集和合成数据集上得到了验证，实验结果表明我们的方法可以在模型中高精度地保留弯曲的壁结构和详细结构。