Indoor camera pose estimation via style‐transfer 3D models

Many vision-based indoor localization methods require tedious and comprehensive pre-mapping of built environments. This research proposes a mapping-free approach to estimating indoor camera poses based on a 3D style-transferred building information model (BIM) and photogrammetry technique. To address the cross-domain gap between virtual 3D models and real-life photographs, a CycleGAN model was developed to transform BIM renderings into photorealistic images. A photogrammetry-based algorithm was developed to estimate camera pose using the visual and spatial information extracted from the style-transferred BIM. The experiments demonstrated the efficacy of CycleGAN in bridging the cross-domain gap, which significantly improved performance in terms of image retrieval and feature correspondence detection. With the 3D coordinates retrieved from BIM, the proposed method can achieve near real-time camera pose estimation with an accuracy of 1.38 m and 10.1° in indoor environments.