CubiCasa5K: A Dataset and an Improved Multi-Task Model for Floorplan Image Analysis

Kalervo, Ahti; Ylioinas, Juha; Häikiö, Markus; Karhu, Antti; Kannala, Juho

doi:10.48550/arxiv.1904.01920

Cited by 3 publications

(2 citation statements)

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“…The aim of the Graph2Plan [27] project is to produce floor plans from user information, and the CubiCasa5k [28] and CubiGraph5k [29] projects are able to separate the floor plan designs of each room into polygons, and then create the corresponding floor plan graph. A serious drawback of the various current projects is that they are focused and trained on the floor plans of houses, with few rooms, i.e., they are simpler than the floor plans used in our work.…”

Section: Related Workmentioning

confidence: 99%

EvacuAI: An Analysis of Escape Routes in Indoor Environments with the Aid of Reinforcement Learning

Rosa,

Falqueiro,

Bonacin

et al. 2023

Sensors

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There is only a very short reaction time for people to find the best way out of a building in a fire outbreak. Software applications can be used to assist the rapid evacuation of people from the building; however, this is an arduous task, which requires an understanding of advanced technologies. Since well-known pathway algorithms (such as, Dijkstra, Bellman–Ford, and A*) can lead to serious performance problems, when it comes to multi-objective problems, we decided to make use of deep reinforcement learning techniques. A wide range of strategies including a random initialization of replay buffer and transfer learning were assessed in three projects involving schools of different sizes. The results showed the proposal was viable and that in most cases the performance of transfer learning was superior, enabling the learning agent to be trained in times shorter than 1 min, with 100% accuracy in the routes. In addition, the study raised challenges that had to be faced in the future.

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Section: Related Workmentioning

confidence: 99%

EvacuAI: An Analysis of Escape Routes in Indoor Environments with the Aid of Reinforcement Learning

Rosa,

Falqueiro,

Bonacin

et al. 2023

Sensors

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“…Some researchers have used object detection to detect components in floor plans, identifying their locations with bounding boxes and class labels [2][3][4][5]. Other researchers used instance segmentation instead, with their system's output location predictions with masks and class labels [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Interoperability between Deep Neural Networks and 3D Architectural Modeling Software: Affordances of Detection and Segmentation

Wei,

Gupta,

Czerniawski

2023

Buildings

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Building owners are working on converting their legacy documentation 2D floor plans into digital 3D representations, but the manual process is labor-intensive and time-consuming. In this paper, deep learning is leveraged to automate the process. This automation requires interoperability between artificial neural networks and prevailing 3D modeling software. The system processes 2D floor plans and outputs parameters of recognized walls, single doors, double doors, and columns. The parameters include the start point and end point of the wall and the center point of the door and column. These parameters are input into Revit 2022 through the Revit API 2022 after post-processing. The dimensional parameter integration affordances of object detection and instance segmentation are studied and compared using Faster R-CNN and Mask R-CNN models. Instance segmentation was found to require more time for data labeling but was more capable of informing the modeling of irregularly shaped objects. The mean Average Precision (mAP) of object detection and instance segmentation are 71.7% and 69.3%, respectively. Apart from single doors, the average precision for other categories falls within the range of 74% to 96%. The results provide software developers with guidance on choosing between object detection and instance segmentation strategies for processing legacy building documents. These types of systems are anticipated to be pivotal to the industry’s transition from 2D to 3D information modalities and advise practitioners to carefully choose suitable models and consider the recommendations provided in this study to mitigate potential failure cases.

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EvacuAI: An analysis of Escape Routes in Indoor Environments with the Aid of Reinforcement Learning

Rosa¹,

Falqueiro²,

Bonacin³

et al. 2023

Preprint

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There is only a very short reaction time for people to find the best way out of a building in a fire outbreak. Software applications can be used to assist the rapid evacuation of victims; however, this is an arduous task, which requires an understanding of advanced technologies. Since well-known pathway algorithms (such as, Dijkstra, Bellman-Ford and A*) can lead to serious problems over performance, we decided to make use of deep reinforcement learning techniques. A wide range of strategies including a random initialization of replay buffer and transfer learning were assessed in three projects involving schools of different sizes. The results showed the proposal was viable and that in most cases the performance of transfer learning was superior. In addition, the study raised challenges that had to be faced in the future.

show abstract

CubiCasa5K: A Dataset and an Improved Multi-Task Model for Floorplan Image Analysis

Cited by 3 publications

References 0 publications

EvacuAI: An Analysis of Escape Routes in Indoor Environments with the Aid of Reinforcement Learning

EvacuAI: An Analysis of Escape Routes in Indoor Environments with the Aid of Reinforcement Learning

Interoperability between Deep Neural Networks and 3D Architectural Modeling Software: Affordances of Detection and Segmentation

EvacuAI: An analysis of Escape Routes in Indoor Environments with the Aid of Reinforcement Learning

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