This paper addresses the challenge of enriching geometric digital twins of buildings, with a particular emphasis on capturing small but important entities from the electrical and the re-safety domain, such as signs, sockets, switches, smoke alarms, etc. Unlike most previous research that focussed on structural elements and processed laser point clouds and images separately, we propose a novel method that fuses laser scanning and photogrammetry methods to capture the relevant objects, recognise them in 2D images and then map these to a 3D space. The considered object classes include electrical elements (light switch, light, speaker, socket, elevator button), safety elements (emergency switch, smoke alarm, re extinguisher, escape sign), plumbing system elements (pipes), and other objects with useful information (door sign, board). Semantic information like class labels is extracted by applying AI-based image segmentation and then mapped to the 3D point cloud, segmenting the point cloud into point clusters. We subsequently fi t geometric primitives to the point clusters and extract text information by AI-based text detection and recognition. The final output of our proposed method is an information-rich digital twin of buildings that contains geometric information, semantic information such as object categories and useful text information which is valuable in many aspects, like condition monitoring, facility maintenance and management. In summary, the paper presents a nearly fully-automated pipeline to enrich a geometric digital twin of buildings with details and provides a comprehensive case study.