Drift-Free BIM Alignment for Mixed Reality Visualization Through Image Style Transfer and Feature Matching

Accurate localization is a persistent challenge for Mixed Reality (MR) applications in the construction industry, where reliable alignment between digital building models and physical environments is critical. Commercial MR devices such as the Microsoft HoloLens rely on Visual-Inertial Simultaneous Localization and Mapping (VISLAM) for pose estimation, but accumulated drift over extended trajectories and visually ambiguous indoor spaces often reduces localization accuracy. This paper presents a complementary localization refinement methodology that integrates HoloLens spatial tracking with image style transfer and geometry-based pose estimation for Building Information Modeling (BIM)-aligned MR visualization. Image style transfer is used to reduce appearance discrepancies between real-world images and synthetic BIM renderings, improving feature correspondence for geometric alignment. Pose refinement is then applied using feature matching and Perspective-n-Point (PnP) estimation to mitigate accumulated drift when sufficient visual evidence is available. The method is evaluated using 1408 image pairs captured along an indoor trajectory, demonstrating improved BIM alignment, significantly reducing accumulated drift to 1–2 pixels. The proposed approach supports more reliable MR visualization for construction-related tasks such as inspection, coordination, and spatial decision-making.