Multi-Unit Floor Plan Recognition and Reconstruction

Multi-Unit Floor Plan Recognition and Reconstruction

de Jong, Gijs (TU Delft Electrical Engineering, Mathematics and Computer Science)

Rellermeyer, Jan S. (mentor)
Pouwelse, J.A. (mentor)

Delft University of Technology

Computer Science

2022-08-25

Automatically deriving 3D representations of buildings is a challenging problem which is at the base of a wide range of applications. The DE-RISC project aims to generate a 3D model of the entire city of Rotterdam in The Netherlands, enabling many of these applications. Generating a 3D model of a building can be done in a variety of ways, of which only few are robust, scalable and generalizable. Recognition and reconstruction of architectural floor plans is such a scalable method long researched in literature.
Although these methods generalized poorly initially, recent breakthroughs in computer vision have allowed for the application of deep learning based approaches. Recent floor plan processing methods have shown promising results on single-unit floor plans. Single-unit floor plans are floor plans of single apartments of relatively low complexity. In contrast, multi-unit floor plans describe entire buildings, and are thus significantly larger and of higher complexity. Applying single-unit floor plan processing methods to multi-unit floor plans is not trivial, and results in insufficient accuracy. These methods can therefore not be applied to an entire city, limiting the scalability and generalizability.
This thesis proposes a novel multi-scale floor plan recognition and reconstruction method designed to transform floor plans of arbitrary size into their 3D representations. As no multi-unit floor plan datasets exists, a novel floor plan dataset MURF is presented based on multi-unit floor plans from buildings in Rotterdam. MURF considers seven boundary and opening semantic classes, each with distinct physical properties. The recognition part of the method relies on an FCN employing multi-scale skip-connections, an attention mechanism, and a multi-task training objective to reinforce the learning of multi-scale features. The reconstruction part refines predictions from the recognition step by applying post-processing, vectorization, and visualization in Blender.
The proposed method is compared to floor plan processing models from literature and general stateof-the-art segmentation models by a quantitative and qualitative evaluation. Experimental results show that the proposed method is significantly outperforms existing floor plan processing methods, and performs best out of general segmentation models. A case study on the EMC in Rotterdam demonstrates the generalizability of the proposed method.

Floor plan analysis
FCN
Multi-task learning

http://resolver.tudelft.nl/uuid:158f6745-0b43-4796-b21d-6388a35f5a2d

Student theses

master thesis

© 2022 Gijs de Jong