Image matching algorithms for VR panoramic solutions for interior design

Currently, algorithms for matching virtual reality panoramic solutions in interior design are overly reliant on text, resulting in low matching efficiency. To address these shortcomings, the study proposes a virtual reality panoramic solution image matching algorithm based on convolutional neural networks and deep learning. The results show that the proposed algorithm has the shortest processing time, at just 2.3 seconds, and achieves the highest accuracy rate of 93.1%. In further category-specific tests, the algorithm maintains an accuracy rate above 91% across all categories, with no significant imbalance observed across different categories. When images are occluded, the algorithm's precision and recall rates are 87.5% and 81%, respectively. When the image background is complex, these values are 87.8% and 80.5%, respectively. These results indicate that the proposed algorithm has further applied deep learning methods in the field of interior design, driving the industry toward greater digitalization and intelligence.