Scan2Plan: Efficient Floorplan Generation from 3D Scans of Indoor Scenes

Phalak, Ameya; Badrinarayanan, Vijay; Rabinovich, Andrew

doi:10.48550/arxiv.2003.07356

Cited by 7 publications

(9 citation statements)

References 34 publications

(56 reference statements)

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“…Other nontraditional ML applications seek to generate floor plans from point clouds captured inside of buildings. By reformulating the problem to predict the center of each room and wall for every point in the cloud, the problem aligns well with existing neural network approaches and allows for interpretable debugging during development [53].…”

Section: Directions For Future Researchmentioning

confidence: 78%

Mind the Gap: On Bridging the Semantic Gap between Machine Learning and Information Security

Smith,

Johnson,

Ingram

et al. 2020

Preprint

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Despite the potential of Machine learning (ML) to learn the behavior of malware, detect novel malware samples, and significantly improve information security (InfoSec) we see few, if any, high-impact ML techniques in deployed systems, notwithstanding multiple reported successes in open literature. We hypothesize that the failure of ML in making high-impacts in InfoSec are rooted in a disconnect between the two communities as evidenced by a semantic gap-a difference in how executables are described (e.g. the data and features extracted from the data). Specifically, current datasets and representations used by ML are not suitable for learning the behaviors of an executable and differ significantly from those used by the InfoSec community. In this paper, we survey existing datasets used for classifying malware by ML algorithms and the features that are extracted from the data. We observe that: 1) the current set of extracted features are primarily syntactic, not behavioral, 2) datasets generally contain extreme exemplars producing a dataset in which it is easy to discriminate classes, and 3) the datasets provide significantly different representations of the data encountered in real-world systems. For ML to make more of an impact in the InfoSec community requires a change in the data (including the features and labels) that is used to bridge the current semantic gap. As a first step in enabling more behavioral analyses, we label existing malware datasets with behavioral features using open-source threat reports associated with malware families. This behavioral labeling alters the analysis from identifying intent (e.g. good vs bad) or malware family membership to an analysis of which behaviors are exhibited by an executable. We offer the annotations with the hope of inspiring future improvements in the data that will further bridge the semantic gap between the ML and InfoSec communities.

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Section: Directions For Future Researchmentioning

confidence: 78%

Mind the Gap: On Bridging the Semantic Gap between Machine Learning and Information Security

Smith,

Johnson,

Ingram

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…This field has since been enriched with methods for room segmentation [17]- [20], opening detection [19]- [21], and reconstruction of curved elements [22] to gain more accurate space models. More recent approaches use machine learning methods to address more intricate edge cases in extracting a floorplan of an indoor environment from 3D scans with fewer restrictions and assumptions regarding the shape of the space [18], [23], [24].…”

Section: A Related Workmentioning

confidence: 99%

Advancing Indoor Environment Modeling: A Comparative Study of Evolutionary Algorithms for Optimal Sensor Placement

Banov,

Pinčić,

Lenac

et al. 2024

Preprint

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This study advances indoor environment modeling by focusing on the optimal placement of sensors. Our approach involves creating a detailed environment model from a 3D point cloud by identifying spatial boundaries and furniture in indoor spaces, which are then represented as a series of polygons. To validate our method, we compare its performance against ground truth data, demonstrating high accuracy in both simple and complex environments. The core of our study is a comprehensive experiment that evaluates the effectiveness of three evolutionary nature-inspired genetic and three metaheuristic iterative optimization algorithms in solving the sensor placement problem in a complex environment scenario. We perform a statistical analysis to understand the impact of the choice of optimization algorithm and the number of sensors on the achieved spatial coverage. This analysis provides insights into the comparative effectiveness of various evolutionary algorithms in enhancing sensor network design within intricate indoor spaces. In particular, the Artificial Bee Colony algorithm consistently delivered superior results.

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“…Additionally, a toolbox for converting floor plan images to a vector format was developed in [21]. Furthermore, generating floor plans using graphs [22], [23], panoramic images [24], or 3D scans [25]- [30] is emerging. Generating furniture layouts using graphs was also discussed in [31].…”

Section: B Real Estate Tasks Using Floor Planmentioning

confidence: 99%

Subjective Functionality and Comfort Prediction for Apartment Floor Plans and Its Application to Intuitive Online Property Search

Narahara

Yamasaki

2023

IEEE Trans. Multimedia

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This paper presents a new user experience for online apartment search using functionality and comfort as query items. Specifically, it has three technical contributions. First, we present a new dataset on the perceived functionality and comfort scores of residential floor plans using nine question statements about the level of comfort, openness, privacy, etc. Second, we propose an algorithm to predict the scores from the floor plan images. Lastly, we implement a new apartment search system and conduct a large-scale usability study using crowdsourcing. The experimental results show that our apartment search system can provide a better user experience. To the best of our knowledge, this is the first work to propose a highly accurate machine learning model for predicting the subjective functionality and comfort of apartments.

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Scan2Plan: Efficient Floorplan Generation from 3D Scans of Indoor Scenes

Cited by 7 publications

References 34 publications

Mind the Gap: On Bridging the Semantic Gap between Machine Learning and Information Security

Mind the Gap: On Bridging the Semantic Gap between Machine Learning and Information Security

Advancing Indoor Environment Modeling: A Comparative Study of Evolutionary Algorithms for Optimal Sensor Placement

Subjective Functionality and Comfort Prediction for Apartment Floor Plans and Its Application to Intuitive Online Property Search

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