# Fast and Regularized Reconstruction of Building Faccades from Street-View Images using Binary Integer Programming

arXiv: https://arxiv.org/abs/2002.08549 · HTML: https://mlsanity.com/paper/2002.08549/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Feb 2020.

## Built on by (most cited first, 6 of 6)

- [Semi-Supervised Adversarial Recognition of Refined Window Structures for Inverse Procedural Faccade Modeling](https://mlsanity.com/paper/2201.08977/) (2022)
- [Computer vision-based analysis of buildings and built environments: A systematic review of current approaches](https://mlsanity.com/paper/2208.00881/) (2022)
- [Window Detection In Facade Imagery: A Deep Learning Approach Using Mask R-CNN](https://mlsanity.com/paper/2107.10006/) (2021)
- [Semi-Supervised Representative Region Texture Extraction of Faccade](https://mlsanity.com/paper/2212.02220/) (2022)
- [Semantic Image Translation for Repairing the Texture Defects of Building Models](https://mlsanity.com/paper/2303.17418/) (2023)
- [StructuredMesh: 3D Structured Optimization of Faccade Components on Photogrammetric Mesh Models using Binary Integer Programming](https://mlsanity.com/paper/2306.04184/) (2023)

## Abstract (arXiv)

Regularized arrangement of primitives on building faccades to aligned locations and consistent sizes is important towards structured reconstruction of urban environment. Mixed integer linear programing was used to solve the problem, however, it is extreamly time consuming even for state-of-the-art commercial solvers. Aiming to alleviate this issue, we cast the problem into binary integer programming, which omits the requirements for real value parameters and is more efficient to be solved . Firstly, the bounding boxes of the primitives are detected using the YOLOv3 architecture in real-time. Secondly, the coordinates of the upper left corners and the sizes of the bounding boxes are automatically clustered in a binary integer programming optimization, which jointly considers the geometric fitness, regularity and additional constraints; this step does not require a priori knowledge, such as the number of clusters or pre-defined grammars. Finally, the regularized bounding boxes can be directly used to guide the faccade reconstruction in an interactive envinronment. Experimental evaluations have revealed that the accuracies for the extraction of primitives are above 0.82, which is sufficient for the following 3D reconstruction. The proposed approach only takes about  10%  to  20%  of the runtime than previous approach and reduces the diversity of the bounding boxes to about 20% to 50%.