Creating indoor space model based on single RGB image with the segmentation of surface free to move

Michail Pirogov; Yuri Karyakin

doi:10.1117/12.3035832

31 July 2024 Creating indoor space model based on single RGB image with the segmentation of surface free to move

Michail Pirogov, Yuri Karyakin

Proceedings Volume 13217, Third International Conference on Digital Technologies, Optics, and Materials Science (DTIEE 2024); 132170K (2024) https://doi.org/10.1117/12.3035832
Event: Third International Conference on Digital Technologies, Optics, and Materials Science (DTIEE 2024), 2024, Fergana and Bukhara, Uzbekistan

Abstract

This paper suggests using existing neural networks and photogrammetry methods to build a point cloud model of a room and highlight floor surfaces. The work uses neural networks for segmentation: Boundary-guided Context Aggregation Network, Deep Convolutional Neural Networks with a fully connected Conditional Random Field, Spatial information Guided Convolutional Network and Swin Transformer with Shifted Windows. Also work uses DPT-L for depth prediction. The proposed ensemble demonstrated the viability of the concept. Three shortcomings were also identified: high peak load on hardware, sensitivity to the color palette of the input image, and the need for fine-tuning. When solving or reducing the impact of identified problems, the ensemble can be used for remotely controlled land-based unmanned vehicle.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Michail Pirogov and Yuri Karyakin "Creating indoor space model based on single RGB image with the segmentation of surface free to move", Proc. SPIE 13217, Third International Conference on Digital Technologies, Optics, and Materials Science (DTIEE 2024), 132170K (31 July 2024); https://doi.org/10.1117/12.3035832

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KEYWORDS

Image segmentation

RGB color model

Matrices

Point clouds

Deep convolutional neural networks

Depth maps

Cameras

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