MonteFloor：扩展MCTS重建精确的大比例尺平面图
MonteFloor: Extending MCTS for Reconstructing Accurate Large-Scale Floor Plans
摘要：
我们提出了一种新的方法，从嘈杂的3D点云重建平面图。我们的主要贡献是一个原则性的方法，依赖于蒙特卡罗树搜索（MCTS）算法，以最大限度地提高一个合适的目标函数有效地，尽管问题的复杂性。与以前的工作一样，我们首先将输入的点云投影到顶视图上，以创建密度图并从中提取房间建议。我们的方法选择并优化这些房间建议的多边形形状，以适应密度图，并输出精确的矢量化楼层图，即使是大型复杂场景。为此，我们调整了MCTS，一种最初设计用于学习玩游戏的算法，通过最大化目标函数来选择房间提案，该目标函数将适应度与深度网络预测的密度图相结合，并对房间形状进行正则化。我们还向MCTS引入了一个细化步骤，用于调整房间建议的形状。对于这一步，我们提出了一种新的微分方法来渲染这些建议的多边形形状。我们评估我们的方法在最近的和具有挑战性的Structured 3D和Floor-SP数据集，并显示出显着的改进，在最先进的，没有施加任何硬约束，也没有假设的平面图配置。
英文摘要：
We propose a novel method for reconstructing floor plans from noisy 3D point clouds. Our main contribution is a principled approach that relies on the Monte Carlo Tree Search (MCTS) algorithm to maximize a suitable objective function efficiently despite the complexity of the problem. Like previous work, we first project the input point cloud to a top view to create a density map and extract room proposals from it. Our method selects and optimizes the polygonal shapes of these room proposals jointly to fit the density map and outputs an accurate vectorized floor map even for large complex scenes. To do this, we adapted MCTS, an algorithm originally designed to learn to play games, to select the room proposals by maximizing an objective function combining the fitness with the density map as predicted by a deep network and regularizing terms on the room shapes. We also introduce a refinement step to MCTS that adjusts the shape of the room proposals. For this step, we propose a novel differentiable method for rendering the polygonal shapes of these proposals. We evaluate our method on the recent and challenging Structured3D and Floor-SP datasets and show a significant improvement over the state-of-the-art, without imposing any hard constraints nor assumptions on the floor plan configurations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
