PanoWalker: Iterative Persistent Panoramic Scene Construction

3D scene reconstruction is increasingly required in real-world mobile capture, where users progressively explore environments and casually acquire observations along a navigation trajectory. Such capture calls for a scene representation that can grow as observations arrive. Panoramic imagery is especially attractive in this regime as each observation captures the full surrounding environment. We present PanoWalker, a lightweight, training-free pipeline for iterative panoramic 3D scene reconstruction. Given a sequence of panoramic pairs, PanoWalker uses a frozen panoramic feed-forward backbone to predict one local Gaussian set for each pair and then incrementally fuses these local predictions into a persistent 3D Gaussian scene. The persistent scene retains the source identity of each pairwise prediction. Optional geometric cleanup screens incoming candidates and constrains the accumulated scene around the capture trajectory; the indoor headline setting disables these filters. At rendering time, source-distance opacity reweighting improves the evaluated near-input HM3D views without changing the stored scene. The pipeline uses frozen backbones and extends the scene without re-predicting earlier pairs or performing per-scene optimization. It leads on the HM3D and Replica aggregate benchmarks, while outdoor final-quality results are mixed. In a separate progressive-preview task on two 360Loc segments, it delivers three successive scenes from 10, 20, and 30 panoramas in 126 s on average. Under approximately the same cumulative time budget, its final WS-PSNR is 17.61 dB, compared with 14.42/13.67 dB for continued ODGS/360GS optimization. The code and model will be made publicly available.