PurposeBIM technology has a huge potential for improving the renovation efficiency for as-built buildings. However, due to the absence of raw design drawings and the complex interior environment, it is difficult to implement 3D reconstruction of building interiors in interior renovation projects. Therefore, this study proposes a 3D reconstruction framework of building interiors, with an aim to generate building interiors building information modeling (BIM) models quickly and accurately based on scan-to-BIM and generative design.Design/methodology/approachThe proposed framework begins by reconstructing interior structured elements based on the scan-to-BIM process including collecting accurate information of as-built buildings by laser scanning, obtaining point clouds of structured elements through deep learning and developing an efficient dynamo algorithm workflow for generating structured elements BIM model. For unstructured elements, intelligent layout design and efficient BIM generation are conducted by combining the BIM tools and generative design.FindingsThe successful implementation of the proposed framework in a conference room demonstrated the feasibility of the proposed framework. The semantic segmentation scheme based on deep learning also exhibited excellent recognition and high efficiency for interior structured elements. Furthermore, it is proved that the combination of scan-to-BIM and generative design has high application value in the 3D reconstruction of building interiors.Originality/valueOn one hand, a feasible framework is proposed to generate BIM model of building interiors, improve interoperability among different software tools, streamline the complexity of the scan-to-BIM process and meet the reconfiguration requirement of unstructured elements layout scheme in interior renovation projects. On the other hand, the use of BIM and various emerging technologies can drive digital transformation and further advance the industrialization process of interior renovation in as-built buildings.