论文标题
Scan2plan：从3D扫描室内场景中产生有效的平面图
Scan2Plan: Efficient Floorplan Generation from 3D Scans of Indoor Scenes
论文作者
论文摘要
我们介绍了Scan2Plan，这是一种从3D扫描室内环境的结构元素中准确估算平面图的新型方法。所提出的方法结合了两阶段的方法，其中初始阶段将场景的无序点云表示为房间实例和墙壁实例，并使用基于神经网络的投票方法。随后的阶段通过在预测的房间和墙壁关键点上找到最短的路径，估计了每个房间的简单多边形参数的闭合周长。最终的平面图只是全球坐标系统中所有此类房间周围的组装。 SCAN2PLAN管道可为复杂布局提供准确的平面图，与现有方法相比，高度可行且非常有效。投票模块仅在合成数据上进行培训，并对可公开的结构3D和BKE数据集进行评估，以证明出色的定性和定量结果优于最先进的技术。
We introduce Scan2Plan, a novel approach for accurate estimation of a floorplan from a 3D scan of the structural elements of indoor environments. The proposed method incorporates a two-stage approach where the initial stage clusters an unordered point cloud representation of the scene into room instances and wall instances using a deep neural network based voting approach. The subsequent stage estimates a closed perimeter, parameterized by a simple polygon, for each individual room by finding the shortest path along the predicted room and wall keypoints. The final floorplan is simply an assembly of all such room perimeters in the global co-ordinate system. The Scan2Plan pipeline produces accurate floorplans for complex layouts, is highly parallelizable and extremely efficient compared to existing methods. The voting module is trained only on synthetic data and evaluated on publicly available Structured3D and BKE datasets to demonstrate excellent qualitative and quantitative results outperforming state-of-the-art techniques.
