Earticle

초록

영어

As the number of single-person households increases in South Korea, there is a growing demand for more personalized and space-efficient interior design, particularly among the MZ generation who value individuality. Recently, AI-powered services are being developed for efficient interior design. These services utilize indoor photographs to create digital twin-based 3D interior design programs. However, the quality of service varies significantly depending on the algorithm used. In response to this challenge, this study compares and analyzes the image segmentation performance of Grounded SAM and FastSAM, both derived from the Segment Anything Model (SAM) announced by Meta in early 2023. The ADE20K dataset, related to interior design, and the DAVIS2016 dataset, which focuses on single-object segmentation, were used to evaluate the accuracy and processing speed of the two models and to explore their applicability in real-world interior design workflows. The experimental results shows that Grounded SAM outperforms FastSAM in terms of object recognition accuracy. This research will offer valuable criteria for model selection in the automation of interior design and the development of AR/VR applications.

목차

Abstract
I. INTRODUCTION
II. RELATED WORK
A. Segment Anything Model (SAM)
B. Grounded SAM
C. FastSAM
III. METHODOLOGY
A. Dataset
B. Performance Evaluation
C. Baseline Models
D. Experimental Design
IV. RESULTS AND DISCUSSION
A. Accuracy Comparison (mIoU)
B. Processing Speed Comparison
C. Practical Evaluation and Model Application Scenarios
V. CONCLUSION
REFERENCES

저자

• Hoijoo Kim [ Dept. of Data Science Graduate School, Dankook University ]
• Mingu Kang [ Dept. of Metaverse Convergencee Graduate School, Dankook University ]
• Sohyun Park [ Dept. of Software School of SW Convergence, Dankook University ]
• Eung-Kyo Suh [ Dept. of Data Science Graduate School, Dankook University Yongin, South Korea ] Corresponding Author

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