  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.
