StructScan3D v1: A First RGB-D Dataset for Indoor Building Elements Segmentation and BIM Modeling
The integration of computer vision and deep learning into Building Information Modeling (BIM) workflows has created a growing need for structured datasets that enable the semantic segmentation of indoor building elements. This paper presents StructScan3D v1, the first version of an RGB-D dataset spe...
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MDPI AG
2025-05-01
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| Online Access: | https://www.mdpi.com/1424-8220/25/11/3461 |
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| author | Ishraq Rached Rafika Hajji Tania Landes Rashid Haffadi |
| author_facet | Ishraq Rached Rafika Hajji Tania Landes Rashid Haffadi |
| author_sort | Ishraq Rached |
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| description | The integration of computer vision and deep learning into Building Information Modeling (BIM) workflows has created a growing need for structured datasets that enable the semantic segmentation of indoor building elements. This paper presents StructScan3D v1, the first version of an RGB-D dataset specifically designed to facilitate the automated segmentation and modeling of architectural and structural components. Captured using the Kinect Azure sensor, StructScan3D v1 comprises 2594 annotated frames from diverse indoor environments, including residential and office spaces. The dataset focuses on six key building elements: walls, floors, ceilings, windows, doors, and miscellaneous objects. To establish a benchmark for indoor RGB-D semantic segmentation, we evaluate D-Former, a transformer-based model that leverages self-attention mechanisms for enhanced spatial understanding. Additionally, we compare its performance against state-of-the-art models such as Gemini and TokenFusion, providing a comprehensive analysis of segmentation accuracy. Experimental results show that D-Former achieves a mean Intersection over Union (mIoU) of 67.5%, demonstrating strong segmentation capabilities despite challenges like occlusions and depth variations. As an evolving dataset, StructScan3D v1 lays the foundation for future expansions, including increased scene diversity and refined annotations. By bridging the gap between deep learning-driven segmentation and real-world BIM applications, this dataset provides researchers and practitioners with a valuable resource for advancing indoor scene reconstruction, robotics, and augmented reality. |
| format | Article |
| id | doaj-art-dd1ba4f18ea64c00b27f6f6f79371ec6 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj-art-dd1ba4f18ea64c00b27f6f6f79371ec62025-08-20T03:11:32ZengMDPI AGSensors1424-82202025-05-012511346110.3390/s25113461StructScan3D v1: A First RGB-D Dataset for Indoor Building Elements Segmentation and BIM ModelingIshraq Rached0Rafika Hajji1Tania Landes2Rashid Haffadi3College of Geomatic Sciences and Surveying Engineering, Institute of Agronomy and Veterinary Medicine, Rabat 6202, MoroccoCollege of Geomatic Sciences and Surveying Engineering, Institute of Agronomy and Veterinary Medicine, Rabat 6202, MoroccoICube Laboratory UMR 7357, Photogrammetry and Geomatics Group, National Institute of Applied Sciences (INSA Strasbourg), 24, Boulevard de la Victoire, 67084 Strasbourg, FranceGEOPTIMA, B4, Med El Amraoui Street, Corner of Sebou Street, Office 4, Kenitra, MoroccoThe integration of computer vision and deep learning into Building Information Modeling (BIM) workflows has created a growing need for structured datasets that enable the semantic segmentation of indoor building elements. This paper presents StructScan3D v1, the first version of an RGB-D dataset specifically designed to facilitate the automated segmentation and modeling of architectural and structural components. Captured using the Kinect Azure sensor, StructScan3D v1 comprises 2594 annotated frames from diverse indoor environments, including residential and office spaces. The dataset focuses on six key building elements: walls, floors, ceilings, windows, doors, and miscellaneous objects. To establish a benchmark for indoor RGB-D semantic segmentation, we evaluate D-Former, a transformer-based model that leverages self-attention mechanisms for enhanced spatial understanding. Additionally, we compare its performance against state-of-the-art models such as Gemini and TokenFusion, providing a comprehensive analysis of segmentation accuracy. Experimental results show that D-Former achieves a mean Intersection over Union (mIoU) of 67.5%, demonstrating strong segmentation capabilities despite challenges like occlusions and depth variations. As an evolving dataset, StructScan3D v1 lays the foundation for future expansions, including increased scene diversity and refined annotations. By bridging the gap between deep learning-driven segmentation and real-world BIM applications, this dataset provides researchers and practitioners with a valuable resource for advancing indoor scene reconstruction, robotics, and augmented reality.https://www.mdpi.com/1424-8220/25/11/3461semantic segmentationRGB-DKinect AzureBIMstructural elementsdataset |
| spellingShingle | Ishraq Rached Rafika Hajji Tania Landes Rashid Haffadi StructScan3D v1: A First RGB-D Dataset for Indoor Building Elements Segmentation and BIM Modeling Sensors semantic segmentation RGB-D Kinect Azure BIM structural elements dataset |
| title | StructScan3D v1: A First RGB-D Dataset for Indoor Building Elements Segmentation and BIM Modeling |
| title_full | StructScan3D v1: A First RGB-D Dataset for Indoor Building Elements Segmentation and BIM Modeling |
| title_fullStr | StructScan3D v1: A First RGB-D Dataset for Indoor Building Elements Segmentation and BIM Modeling |
| title_full_unstemmed | StructScan3D v1: A First RGB-D Dataset for Indoor Building Elements Segmentation and BIM Modeling |
| title_short | StructScan3D v1: A First RGB-D Dataset for Indoor Building Elements Segmentation and BIM Modeling |
| title_sort | structscan3d v1 a first rgb d dataset for indoor building elements segmentation and bim modeling |
| topic | semantic segmentation RGB-D Kinect Azure BIM structural elements dataset |
| url | https://www.mdpi.com/1424-8220/25/11/3461 |
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