Research on automatic layout algorithm of indoor space based on computer vision

This paper proposes an indoor space automatic layout algorithm based on computer vision, which aims to achieve efficient and reasonable indoor furniture layout. By setting up the layout optimization model, combining the constraints of space utilization and furniture distribution, the layout issue is numerized as a mathematical formula. On this basis, a deep learning method combining convolutional neural network and generative adversarial network is established to extract indoor scene image features and generate layout solutions. The results of the performance test show that the algorithm achieves good performance in accuracy, space utilization and computational efficiency, with great superiority over rule-based and traditional generation methods. Through visual analysis and quantitative indicators, this study verifies the effectiveness of the algorithm, demonstrates the application potential of computer vision technology in interior design automation, and provides innovative solutions for intelligent space planning.