Automatic Reconstruction of Semantic 3D Models from 2D Floor Plans

TL;DR

This paper introduces an automated pipeline to convert scanned 2D floor plans into semantic 3D models, significantly improving efficiency and accuracy over manual reconstruction, with state-of-the-art results on public datasets.

Contribution

The authors propose a novel vectorization method for converting 2D plans to 3D models, outperforming previous approaches and demonstrating good generalization.

Findings

Achieves state-of-the-art results on CubiCasa5k dataset.

Outperforms previous methods in vectorization accuracy.

Shows good generalization to various plan types.

Abstract

Digitalization of existing buildings and the creation of 3D BIM models for them has become crucial for many tasks. Of particular importance are floor plans, which contain information about building layouts and are vital for processes such as construction, maintenance or refurbishing. However, this data is not always available in digital form, especially for older buildings constructed before CAD tools were widely available, or lacks semantic information. The digitalization of such information usually requires manual work of an expert that must reconstruct the layouts by hand, which is a cumbersome and error-prone process. In this paper, we present a pipeline for reconstruction of vectorized 3D models from scanned 2D plans, aiming at increasing the efficiency of this process. The method presented achieves state-of-the-art results in the public dataset CubiCasa5k, and shows good generalization to different types of plans. Our vectorization approach is particularly effective, outperforming previous methods.