3D Residential Reconstruction from a Single Indoor Floor Plan

3D residential reconstruction plays a crucial role in various fields, such as architecture, interior design, virtual reality, and robot navigation. While existing methods typically rely on complex data acquisition setups or multiple images, we proposed a novel method for 3D residential reconstruction from a single indoor floor plan. By leveraging the information encoded in an indoor floor plan, our proposed method utilizes traditional and deep-learning-based computer vision techniques to reconstruct the corresponding 3D residential model accurately. Image classification based on deep learning is applied for calculating the scale, and semantic segmentation based on deep learning is employed for extracting walls, doors and windows. By analyzing the complex geometric relationship of skeleton curves, the vectorized representation of the building structure is obtained. An accurate 3D residential model is eventually reconstructed by synthesizing all the obtained information.