MonteFloor: Extending MCTS for Reconstructing Accurate Large-Scale Floor Plans (2103.11161v2)

Abstract: 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.

• The paper introduces an extended MCTS framework that optimizes room proposals using an objective function combining learned IoU predictions with regularization constraints.
• It incorporates a novel differentiable rendering method to refine polygonal room shapes, enhancing reconstruction precision from 3D point clouds.
• Results on Structured3D and Floor-SP datasets demonstrate significant improvements in accuracy and efficiency over previous state-of-the-art methods.

An Overview of MonteFloor: Enhancing Floor Plan Reconstruction via MCTS

The paper, "MonteFloor: Extending MCTS for Reconstructing Accurate Large-Scale Floor Plans," presents an innovative approach to the problem of floor plan reconstruction from 3D point clouds. This research leverages the Monte Carlo Tree Search (MCTS) algorithm to efficiently navigate the complex problem space involved in floor plan reconstruction, achieving significant improvements over existing methods.

Key Contributions

The main contributions of the paper are twofold:

1. Adaptation of MCTS: The authors extend MCTS, traditionally used in game-playing algorithms such as AlphaGo, to the domain of floor plan reconstruction. This adaptation is significant as it utilizes MCTS to select room proposals strategically by optimizing an objective function. This function combines the fitness of room proposals with regularization terms, allowing the system to handle complex scenes robustly.
2. Refinement with Differentiable Rendering: The paper introduces a refinement step within MCTS, which is essential for adjusting room proposal shapes more accurately to fit the density map. A novel differentiable method for rendering the polygonal shapes of these proposals is proposed, facilitating the optimization of room proposal configurations.

Methodology

The methodology follows several key steps:

• Generation of Room Proposals: The method begins by projecting a 3D point cloud into a 2D density map. Room segments are detected using Mask R-CNN, and polygonal room proposals are generated via a Douglas-Peucker algorithm with varying complexity.
• Objective Function and Proposal Selection: MCTS is employed to navigate the space of room proposals. An objective function evaluates each path through the tree, combining a learned metric network—trained to predict the Intersection-over-Union score—with a regularization term favoring contact without overlap and refining angles near 90 degrees.
• Optimization and Differentiable Rendering: The refinement step involves optimizing room proposal shapes using a differentiable winding number algorithm for rendering polygons. This facilitates precise adjustment of the floor plan's final shape.

Results and Implications

The paper demonstrates the effectiveness of MonteFloor on the Structured3D and Floor-SP datasets, showing improvements in precision and recall across room, corner, and angle metrics. The results indicate a significant enhancement in reconstruction quality compared to state-of-the-art methods like Floor-SP, with MonteFloor achieving better accuracy and reduced computation times.

Future Prospects

MonteFloor's approach suggests promising opportunities beyond floor plan reconstruction. Its adaptability to various scene-understanding tasks points to potential applications in robotics, augmented reality, and architectural design, where complex 3D environments need precise and efficient modeling. The research opens avenues for further exploration of MCTS in data-driven scene understanding, optimizing the selection and configuration of multiple related components within complex data spaces.

In conclusion, the MonteFloor approach not only advances the field of floor plan reconstruction but also sets a foundation for broader applications in computer vision, demonstrating the utility of adapting game-solving algorithms like MCTS to other domains involving combinatorial challenges.