A Hybrid Semantic-Geometric Approach for Clutter-Resistant Floorplan Generation from Building Point Clouds - Paper

Oct 24, 2026

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Building Information Modeling (BIM) workflows rely on accurate spatial representations, yet Scan-to-BIM methods often neglect semantic cues and can underperform under clutter and occlusion. Point-level semantic segmentation further fails to directly form object-level structures required for BIM. This work introduces a hybrid semantic-geometric pipeline for clutter-resistant floorplan generation from laser-scanned point clouds, combining preprocessing, PointNet++ semantic labeling, region-growing geometric grouping, and a two-stage RANSAC line parameterization. Evaluation uses precision, recall, IOU, and multiple error metrics.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages8

Reading Time20 min

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ICCEPM 2022 The 9th International Conference on Construction Engineering and Project Management Jun. 20-23, 2022, Las Vegas, NV, USA A Hybrid Semantic-Geometric Approach for Clutter-Resistant Floorplan Generation from Building Point Clouds Seongyong Kim 1, Yosuke Yajima2, Jisoo Park 1, Jingdao Chen3, and Yong K. Cho 1* 1 Civil and Environmental Engineering, Georgia Institute of Technology, 790 Atlantic Dr. NW, Atlanta, GA 30332, USA. Email: [skim3310@gatech.edu](skim3310@gatech.edu), [j](jpark711@gatech.edu)[park711@gatech.edu](jpark711@gatech.edu), [yong.cho@ce.gatech.edu](yong.cho@ce.gatech.edu) (*corresponding author) 2 Institute for Robotics and Intelligent Machines, Georgia Institute of Technology, 801 Atlantic Dr. NW, Atlanta, GA 30332, [USA](USA. Email: yyajima@gatech.edu)[. Email:](USA. Email: yyajima@gatech.edu)[ ](USA. Email: yyajima@gatech.edu)[yyajima@gatech.edu](USA. Email: yyajima@gatech.edu) 3 Computer Science and Engineering, Mississippi State University, Mississippi State, MS 39762, USA. E-mail: [chenjingdao@cse.msstate.edu](chenjingdao@cse.msstate.edu) Abstract: Building Information Modeling (BIM) technology is a key component of modern construction engineering and project management workflows. As-is BIM models that represent the spatial reality of a project site can offer crucial information to stakeholders for construction progress monitoring, error checking, and building maintenance purposes. Geometric methods for automatically converting raw scan data into BIM models (Scan-to-BIM) often fail to make use of higher-level semantic information in the data. Whereas, semantic segmentation methods only output labels at the point level without creating object level models that is necessary for BIM. To address these issues, this research proposes a hybrid semantic-geometric approach for clutterresistant floorplan generation from laser-scanned building point clouds. The input point clouds are first pre-processed by normalizing the coordinate system and removing outliers. Then, a semantic segmentation network based on PointNet++ is used to label each point as ceiling, floor, wall, door, stair, and clutter. The clutter points are removed whereas the wall, door, and stair points are used for 2D floorplan generation. A region-growing segmentation algorithm paired with geometric reasoning rules is applied to group the points together into individual building elements. Finally, a 2-fold Random Sample Consensus (RANSAC) algorithm is applied to parameterize the building elements into 2D lines which are used to create the output floorplan. The proposed method is evaluated using the metrics of precision, recall, Intersection-over-Union (IOU), Betti error, and warping error. Key words: floorplan reconstruction, Scan to BIM, 3D deep learning, classification and segmentation, machine learning 1. INTRODUCTION In Architecture, Construction, and Engineering community, Building Information Modeling (BIM) technology is a key component of modern construction engineering and project management workflows. As-is BIM models that represent the spatial reality of a project site can offer crucial information to stakeholders for construction progress monitoring, error checking, and building maintenance purposes. However, as-planned BIM models may not always be available, and for the important use case of as-built BIM that represents the actual site conditions, the approach of building the model using 3D scan data have recently gained attention. Especially, the 1st Scan-toBIM challenge is recently hosted by the 2021 CV4AEC workshop and provide a publicly-available building point cloud dataset to reconstruct floor plans or BIM models ([https://cv4aec.github.io/](https://cv4aec.github.io/)). This paper will introduce our method, which placed 2nd in the floor plan reconstruction challenge. Geometric methods for automatically converting raw scan data into BIM models (Scan-to-BIM) often fail to make use of higher-level sem

FAQs

What problem does the proposed method address in Scan-to-BIM floorplan generation?

It combines metrics of precision, recall, Intersection-over-Union (IOU), Betti error, and warping error.

Which point categories are used versus discarded during floorplan generation?

The method labels points as ceiling, floor, wall, door, stair, and clutter. Clutter points are removed, while wall, door, and stair points are used for 2D floorplan generation.

How are building elements converted into the final 2D floorplan?

Region-growing segmentation groups points into individual building elements using geometric reasoning rules. A two-fold RANSAC algorithm then parameterizes these elements into 2D lines that are used to construct the output floorplan.

Oct 24, 2026

Upload by Aditya