Approach to Enhancing Panoramic Segmentation in Indoor Construction Sites Based on a Perspective Image Segmentation Foundation Model

Panoramic images in indoor construction sites are gaining attention as valuable tools for process monitoring and quality assessment. However, despite the environmental complexity and the demand for high segmentation performance in indoor construction environments, the scarcity of specialized segmentation models and datasets has created a gap between technological advancements and practical application, thus hindering the effective utilization of panoramic images. To address these challenges, this study proposes a novel approach leveraging the Segment Anything Model (SAM), a perspective image segmentation foundation model, to enhance the performance of existing segmentation models. The proposed method iteratively executes SAM with adjusted input parameters to extract objects of varying sizes and subsequently applies filtering algorithms to retain valid objects. Then, label assignment and merging processes are performed based on the predictions from the target model to improve segmentation accuracy. The experimental study was conducted using Panoplane360, a model specifically designed for plane segmentation, as the target model. A quantitative evaluation was conducted to measure the exactness of label assignment, and two qualitative evaluations were performed to assess whether the assigned labels accurately represent the actual planar information. The evaluation results confirmed that the proposed method significantly improves segmentation performance compared to conventional approaches. The findings of this study highlight the potential of SAM-based methods to enhance segmentation accuracy in dynamic indoor construction environments. Furthermore, the proposed approach provides practical advantages, as it improves segmentation performance without requiring the construction of additional datasets. Future research will focus on resolving computational efficiency issues resulting from iterative SAM execution and will extend the applicability of the proposed approach to diverse segmentation tasks and models.