Semantic Equirectangular Visual Tracking in Lightweight 3D Building Reconstructions

Accurate visual localization often relies on dense, high-fidelity 3D models, which provide rich geometric and photometric detail but are expensive to acquire, heavy to store, and limited in scalability. As an alternative, lightweight city models represent only coarse building volumes, offering compactness, accessibility, and privacy but posing challenges for reliable alignment due to the lack of textures and fine structure. This work addresses these challenges by introducing a semantic equirectangular Gaussian Mixture–based virtual visual servoing approach that aligns real panoramic images with synthetic views rendered from lightweight building models. The method combines semantic building masks with Gaussian Mixtures, a seamless $360^\circ$ formulation, and frequency-domain computation to overcome the poor gradients of direct photometric binary-mask alignment while maintaining computational efficiency. Experiments on outdoor trajectories demonstrate accurate and stable tracking, robustness under frame skipping, and resilience to dynamic occlusions through semantic masking. These results indicate that reliable localization is feasible with coarse city models, providing a scalable alternative to high-fidelity reconstructions and opening perspectives for deeper integration of semantic rules into the localization process.